Establishment of a prognostic model of four genes in gastric cancer based on multiple data sets.
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ABSTRACT:
SUBMITTER: Zhou L
PROVIDER: S-EPMC8124107 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:In order to explore new prediction methods and key genes for gastric cancer. Firstly, we downloaded the 6 original sequencing data of gastric cancer on the Illumina HumanHT-12 platform from Array Expression and Gene Expression Omnibus, and used bioinformatics methods to identify 109 up-regulated genes and 271 down-regulated genes. Further, we performed univariate Cox regression analysis of prognostic-related genes, then used Lasso regression to remove collinearity, and finally used multivariate Cox regression to analyze independent prognostic genes (MT1M, AKR1C2, HEYL, KLK11, EEF1A2, MMP7, THBS1, KRT17, RPESP, CMTM4, UGT2B17, CGNL1, TNFRSF17, REG1A). Based on these, we constructed a prognostic risk proportion signature, and found that patients with high-risk gastric cancer have a high degree of malignancy. Subsequently, we used the GSE15459 data set to verify the signature. By calculating the area under the recipient operator characteristic curve of 5-year survival rate, the test set and verification set are 0.739 and 0.681, respectively, suggesting that the prognostic signature has a moderate prognostic ability. The nomogram is used to visualize the prognostic sig-nature, and the calibration curve verification showed that the prediction accuracy is higher. Finally, we verified the expression and prognosis of the hub gene, and suggested that HEYL, MMP7, THBS1, and KRT17 may be potential prognostic biomarkers.
| S-EPMC8806649 | biostudies-literature
Project description:Autophagy played a significant role in the development of cancer. In this study, we explored the value of autophagy-associated genes in gastric cancer. RNA sequencing and clinical information containing 375 gastric cancer and 32 normal tissues were gathered from the TCGA portal. Then we stochastically allocated the autophagy-associated genes (AAGs) to training and testing groups. Next, we screened the discrepantly expressed AAGs and the prognostic AAGs by Cox regression analysis and Lasso regression analysis. Afterwards, we structured the model by using the prognostic AAGs and plotted Kaplan-Meier (KM) and receiver operating characteristic (ROC) curves to verify the performance of models in both groups. Besides, we utilized Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to explore the molecular mechanisms of AAGs in gastric cancer. Finally, we demonstrated discrepant expression of AAGs within gastric cancer and non-tumor tissues at protein level with immunohistochemistry. 28 discrepantly expressed AAGs were screened from the TCGA database which contained 375 gastric cancer and 32 non-tumor samples. Cox and Lasso regression analyses were performed in training group and then we got 5 prognostic AAGs to establish the prognostic model. The patients who had high risk possessed worse overall survival (OS) both in training group (5-year OS, 47.6% vs 23.1%; P < 0.0001) and test group (5-year OS, 49.2% vs 0%, P=0.019). The proportion under ROC curves (AUC) were significant both in training group and test group (5-year AUC, 0.736 vs 0.809). Through this study, we constructed a model for gastric cancer patients which may provide individual treatment and superior prognosis.
| S-EPMC8085875 | biostudies-literature
Project description:Background: Necroptosis plays a crucial role in the progression of multiple types of cancer. However, the role of necroptosis in gastric cancer (GC) remains unclear. The aim of this study is to establish a necroptosis-related prediction model, which could provide information for treatment monitoring. Methods: The TCGA-STAD cohort was employed to establish a prognostic prediction signature and the GEO dataset was employed for external validation. The correlation between the risk score and the immune landscape, tumor mutational burden (TMB), microsatellite instability (MSI), as well as therapeutic responses of different therapies were analyzed. Results: We constructed a prognostic model based on necroptosis-associated genes (NAGs), and its favorable predictive ability was confirmed in an external cohort. The risk score was confirmed as an independent determinant, and a nomogram was further established for prognosis. A high score implies higher tumor immune microenvironment (TIME) scores and more significant TIME cell infiltration. High-risk patients presented with lower TMB, and low-TMB patients had worse overall survival (OS). Meanwhile, Low-risk scores are characterized by MSI-high (MSI-H), lower Tumor Immune Dysfunction and Exclusion (TIDE) score, and higher immunogenicity in immunophenoscore (IPS) analysis. Conclusion: The developed NAG score provides a novel and effective method for predicting the outcome of GC as well as potential targets for further research.
| S-EPMC9519854 | biostudies-literature
Project description:PurposeGastric cancer (GC) has substantial biological differences between Asian and non-Asian populations, which makes it difficult to have a unified predictive measure for all people. We aimed to identify novel prognostic biomarkers to help predict the prognosis of Asian GC patients.Materials and methodsWe investigated the differential gene expression between GC and normal tissues of GSE66229. Univariate, multivariate and Lasso Cox regression analyses were conducted to establish a four-gene-related prognostic model based on the risk score. The risk score was based on a linear combination of the expression levels of individual genes multiplied by their multivariate Cox regression coefficients. Validation of the prognostic model was conducted using The Cancer Genome Atlas (TCGA) database. A nomogram containing clinical characteristics and the prognostic model was established to predict the prognosis of Asian GC patients.ResultsFour genes (RBPMS2, RGN, PLEKHS1, and CT83) were selected to establish the prognostic model, and it was validated in the TCGA Asian cohort. Receiver operating characteristic analysis confirmed the sensitivity and specificity of the prognostic model. Based on the prognostic model, a nomogram containing clinical characteristics and the prognostic model was established, and Harrell's concordance index of the nomogram for evaluating the overall survival significantly higher than the model only focuses on the pathologic stage (0.74 vs. 0.64, p < 0.001).ConclusionThe four-gene-related prognostic model and the nomogram based on it are reliable tools for predicting the overall survival of Asian GC patients.
| S-EPMC7812008 | biostudies-literature
Project description:BackgroundMitochondrial metabolism and mitochondrial structure were found to be altered in high-grade serous ovarian cancer (HGSOC). The intent of this exploration was to systematically depict the relevance between mitochondrial metabolism-related genes (MMRGs) and the prognosis of HGSOC patients by bioinformatics analysis and establish a prognostic model for HGSOC.MethodsFirst of all, screened differentially expressed genes (DEGs) between TCGA-HGSOC and GTEx-normal by limma, with RNA-seq related HGSOC sourced from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database. Subsequently, expressed MMRGs (DE-MMRGs) were acquired by overlapping DEGs with MMRGs, and an enrichment analysis of DE-MMRGs was performed. Kaplan-Meier (K-M) survival analysis and Cox regression analysis were conducted to validate the genes' prognostic value, Gene Set Enrichment Analysis (GSEA) to elucidate the molecular mechanisms of the risk score, and CIBERSORT algorithm to explore the immuno landscape of HGSOC patients. Finally, a drug sensitivity analysis was made via the Drug Sensitivity in Cancer (GDSC) database.Results436 HGSOC-related DE-MMRGs (222 up-regulated and 214 down-regulated) were observed to participate in multiple metabolic pathways. The study structured a MMRGs-related prognostic signature on the basis of IDO1, TNFAIP8L3, GPAT4, SLC27A1, ACSM3, ECI2, PPT2, and PMVK. Risk score was the independent prognostic element for HGSOC. Highly dangerous population was characterized by significant association with mitochondria-related biological processes, lower immune cell abundance, lower expression of immune checkpoint and antigenic molecules. Besides, 54 drugs associated with eight prognostic genes were obtained. Furthermore, copy number variation was bound up with the 8 prognostic genes in expression levels.ConclusionWe have preliminarily determined the prognostic value of MMRGs in HGSOC as well as relationship between MMRGs and the tumor immune microenvironment.
| S-EPMC10226651 | biostudies-literature
Project description:We examined RNA-Seq data on 211 biological samples from 24 different Arabidopsis experiments carried out by different labs. We grouped the samples according to tissue types, and in each of the groups, we identified genes that are stably expressed across biological samples, treatment conditions, and experiments. We fit a Poisson log-linear mixed-effect model to the read counts for each gene and decomposed the total variance into between-sample, between-treatment and between-experiment variance components. Identifying stably expressed genes is useful for count normalization and differential expression analysis. The variance component analysis that we explore here is a first step towards understanding the sources and nature of the RNA-Seq count variation. When using a numerical measure to identify stably expressed genes, the outcome depends on multiple factors: the background sample set and the reference gene set used for count normalization, the technology used for measuring gene expression, and the specific numerical stability measure used. Since differential expression (DE) is measured by relative frequencies, we argue that DE is a relative concept. We advocate using an explicit reference gene set for count normalization to improve interpretability of DE results, and recommend using a common reference gene set when analyzing multiple RNA-Seq experiments to avoid potential inconsistent conclusions.
| S-EPMC5178351 | biostudies-literature
Project description:Large-scale accumulation of omics data poses a pressing challenge of integrative analysis of multiple data sets in bioinformatics. An open question of such integrative analysis is how to pinpoint consistent but subtle gene activity patterns across studies. Study heterogeneity needs to be addressed carefully for this goal.This paper proposes a regulation probability model-based meta-analysis, jGRP, for identifying differentially expressed genes (DEGs). The method integrates multiple transcriptomics data sets in a gene regulatory space instead of in a gene expression space, which makes it easy to capture and manage data heterogeneity across studies from different laboratories or platforms. Specifically, we transform gene expression profiles into a united gene regulation profile across studies by mathematically defining two gene regulation events between two conditions and estimating their occurring probabilities in a sample. Finally, a novel differential expression statistic is established based on the gene regulation profiles, realizing accurate and flexible identification of DEGs in gene regulation space. We evaluated the proposed method on simulation data and real-world cancer datasets and showed the effectiveness and efficiency of jGRP in identifying DEGs identification in the context of meta-analysis.Data heterogeneity largely influences the performance of meta-analysis of DEGs identification. Existing different meta-analysis methods were revealed to exhibit very different degrees of sensitivity to study heterogeneity. The proposed method, jGRP, can be a standalone tool due to its united framework and controllable way to deal with study heterogeneity.
| S-EPMC5568075 | biostudies-literature
Project description:BackgroundMultiple myeloma (MM) is an incurable and relapse-prone disease with apparently prognostic heterogeneity. At present, the risk stratification of myeloma is still incomplete. Pyroptosis, a type of programmed cell death, has been shown to regulate tumor growth and may have potential prognostic value. However, the role of pyroptosis-related genes (PRGs) in MM remains undetermined. The aims of this study were to identify potential prognostic biomarkers and to construct a predictive model related to PRGs.MethodsSequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Non-negative matrix factorization (NMF) was performed to identify molecular subtype screening. LASSO regression was used to screen for prognostic markers, and then a risk score model was constructed. The Maxstat package was utilized to calculate the optimal cutoff value, according to which patients were divided into a high-risk group and a low-risk group, and the survival curves were plotted using the Kaplan-Meier (K-M) method. Nomograms and calibration curves were established using the rms package.ResultsA total of 33 PRGs were extracted from the TCGA database underlying which 4 MM molecular subtypes were defined. Patients in cluster 1 had poorer survival than those in cluster 2 (p = 0.035). A total of 9 PRGs were screened out as prognostic markers, and the predictive ability of the 9-gene risk score for 3-year survival was best (AUC = 0.658). Patients in the high-risk group had worse survival than those in the low-risk group (p < 0.001), which was consistent with the results verified by the GSE2658 dataset. The nomogram constructed by gender, age, International Staging System (ISS) stage, and risk score had the best prognostic predictive performance with a c-index of 0.721.ConclusionOur model could enhance the predictive ability of ISS staging and give a reference for clinical decision-making. The new, prognostic, and pyroptosis-related markers screened out by us may facilitate the development of novel risk stratification for MM.Clinical trial registrationNot applicable.
| S-EPMC9948482 | biostudies-literature
Project description:Diseases prediction has been performed by machine learning approaches with various biological data. One of the representative data is the gut microbial community, which interacts with the host's immune system. The abundance of a few microorganisms has been used as markers to predict diverse diseases. In this study, we hypothesized that multi-classification using machine learning approach could distinguish the gut microbiome from following six diseases: multiple sclerosis, juvenile idiopathic arthritis, myalgic encephalomyelitis/chronic fatigue syndrome, acquired immune deficiency syndrome, stroke and colorectal cancer. We used the abundance of microorganisms at five taxonomy levels as features in 696 samples collected from different studies to establish the best prediction model. We built classification models based on four multi-class classifiers and two feature selection methods including a forward selection and a backward elimination. As a result, we found that the performance of classification is improved as we use the lower taxonomy levels of features; the highest performance was observed at the genus level. Among four classifiers, LogitBoost-based prediction model outperformed other classifiers. Also, we suggested the optimal feature subsets at the genus-level obtained by backward elimination. We believe the selected feature subsets could be used as markers to distinguish various diseases simultaneously. The finding in this study suggests the potential use of selected features for the diagnosis of several diseases.
| S-EPMC6629854 | biostudies-literature
Project description:BackgroundPancreatic cancer has a 5-year overall survival lower than 8%. Pancreatic adenocarcinoma (PAAD) is the most common type. This study attempted to explore novel molecular subtypes and a prognostic model through analyzing tumor microenvironment (TME).Materials and methodsSingle-cell RNA sequencing (scRNA-seq) data and expression profiles from public databases were downloaded. Three PAAD samples with single-cell data and 566 samples with gene expression data were included. Seurat was used to identify cell subsets. SVA merged and removed batch effects from multichip datasets. CIBERSORT was used to evaluate the components of different cells in transcriptome, ConsensusClusterPlus was used to identify molecular subtypes, and gene set enrichment analysis was used for functional enrichment analysis. LASSO Cox was performed to construct dimensionality reduction and prognosis model.ResultsMemory B cells (MBCs) were identified to be significantly with PAAD prognosis. Two immune subtypes (IS1 and IS2) with distinct overall survival were constructed. Forty-one DEGs were identified between IS1 and IS2. Four prognostic genes (ANLN, ARNTL2, SERPINB5, and DKK1) were screened to develop a prognostic model. The model was effective in classifying samples into high-risk and low-risk groups with distinct prognosis. Three subgroups of MBCs were identified, where MBC_0 and MBC_1 were differentially distributed between IS1 and IS2, high-risk and low-risk groups.ConclusionsMBCs were closely involved in PAAD progression, especially MBC_0 and MBC_1 subgroups. The four-gene prognostic model was predictive of overall survival and could guide immunotherapy for patients with PAAD.
| S-EPMC9253429 | biostudies-literature