Dataset Information

Identification of a molecular signature of prognostic subtypes in diffuse-type gastric cancer.

ABSTRACT: BACKGROUND:Although recent advances in high-throughput technology have provided many insights into gastric cancer (GC), few reliable biomarkers for diffuse-type GC have been identified. Here, we aim to identify a prognostic and predictive signature of diffuse-type GC heterogeneity. METHODS:We analyzed RNA-seq-based transcriptome data to identify a molecular signature in 150 gastric tissue samples including 107 diffuse-type GCs. The predictive value of the signature was verified using other diffuse-type GC samples in three independent cohorts (n?=?466). Log-rank and Cox regression analyses were used to estimate the association between the signature and prognosis. The signature was also characterized by somatic variant analyses and tissue microarray analysis between diffuse-type GC subtypes. RESULTS:Transcriptomic profiling of RNA-seq data identified a signature which revealed distinct subtypes of diffuse-type GC: the intestinal-like (INT) and core diffuse-type (COD) subtypes. The signature showed high predictability and independent clinical utility in diffuse-type GC prognosis in other patient cohorts (HR 2.058, 95% CI 1.53-2.77, P?=?1.76?×?10-6). Integrative mutational and gene expression analyses demonstrated that the COD subtype was responsive to chemotherapy, whereas the INT subtype was responsive to immunotherapy with an immune checkpoint inhibitor (ICI). Tissue microarray analysis showed the practical utility of IGF1 and NXPE2 for predicting diffuse-type GC heterogeneity. CONCLUSIONS:We present a molecular signature that can identify diffuse-type GC patients who display different clinical behaviors as well as responses to chemotherapy or ICI treatment.

SUBMITTER: Kim SK

PROVIDER: S-EPMC7165151 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of a molecular signature of prognostic subtypes in diffuse-type gastric cancer.

Kim Seon-Kyu SK Kim Hee-Jin HJ Park Jong-Lyul JL Heo Haejeong H Kim Seon-Young SY Lee Sang-Il SI Song Kyu-Sang KS Kim Woo-Ho WH Kim Yong Sung YS

Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association 20191126 3

<h4>Background</h4>Although recent advances in high-throughput technology have provided many insights into gastric cancer (GC), few reliable biomarkers for diffuse-type GC have been identified. Here, we aim to identify a prognostic and predictive signature of diffuse-type GC heterogeneity.<h4>Methods</h4>We analyzed RNA-seq-based transcriptome data to identify a molecular signature in 150 gastric tissue samples including 107 diffuse-type GCs. The predictive value of the signature was verified us ...[more]

PMID: 31773340

Similar Datasets

Project description:Gastric cancer (GC), with high heterogeneity, can be mainly classified into intestinal type and diffuse type according to the Lauren classification system. Although a number of differences were reported between these two types, no study on the Lauren subtype-specific multi-gene signature for evaluation of GC prognosis has been conducted, and the molecular mechanism underlying its poor prognosis has still remained elusive. Therefore, this study aimed to explore subtype-specific multi-gene signature for prognostic prediction in different subtypes of Lauren classification. With combination of the least absolute shrinkage and selection operator (LASSO) algorithm and the Akaike information criterion (AIC), the 3-gene subtype-specific prognostic signature was successfully established in diffuse type GC using GSE62254 dataset. Following the calculation of risk score (RS) based on 3-gene signature, the nomogram models were established to predict 1-, 3-, and 5-year overall survival in diffuse type GC. Moreover, the prognostic predictive nomogram model of diffuse type GC was also proved to be effective for validation of GSE1549 dataset and by a Gene Expression Omnibus (GEO)-based meta-analysis. In the analysis of the correlation between RS and clinical-pathological characteristics, RS and two genes of the 3-gene signature (EMCN and COL4A5) were found to be positively correlated with peritoneal metastasis. Furthermore, EMCN and COL4A5, rather than CCL11, were proved to be able to enhance the adhesion ability of MKN45 and NUGC4 cells to peritoneal mesothelial cell line HMR-SV5. Eventually, it was proved that COL4A5 promoted peritoneal metastasis by activating Wnt signaling pathway, whereas the upregulation of integrin family genes mediated by FAK-AKT/ERK/STAT3 signaling pathway activation is involved in peritoneal metastasis promotion function of EMCN. Taken together, our study identified the subtype-specific 3-gene signature in diffuse type GC, which could effectively predict the patients' OS and might explain the molecular mechanisms in presence of its poor prognosis.

Project description:BackgroundGlioblastoma multiforme (GBM) is extensively genetically and transcriptionally heterogeneous, which poses challenges for classification and management. Long noncoding RNAs (lncRNAs) play a critical role in the development and progression of GBM, especially in tumor-associated immune processes. Therefore, it is necessary to develop an immune-related lncRNAs (irlncRNAs) signature.MethodsUnivariate and multivariate Cox regression analyses were utilized to construct a prognostic model. GBM-specific CeRNA and PPI network was constructed to predict lncRNAs targets and evaluate the interactions of immune mRNAs translated proteins. GO and KEGG pathway analyses were used to show the biological functions and pathways of CeRNA network-related immunity genes. Consensus Cluster Plus analysis was used for GBM gene clustering. Then, we evaluated GBM subtype-specific prognostic values, clinical characteristics, genes and pathways, immune infiltration access single cell RNA-seq data, and chemotherapeutics efficacy. The hub genes were finally validated.ResultsA total of 17 prognostically related irlncRNAs were screened to build a prognostic model signature based on six key irlncRNAs. Based on GBM-specific CeRNAs and enrichment analysis, PLAU was predicted as a target of lncRNA-H19 and mainly enriched in the malignant related pathways. GBM subtype-A displayed the most favorable prognosis, high proportion of genes (IDH1, ATRX, and EGFR) mutation, chemoradiotherapy, and low risk and was characterized by low expression of four high-risk lncRNAs (H19, HOTAIRM1, AGAP2-AS1, and AC002456.1) and one mRNA KRT8. GSs with poor survival were mainly infiltrated by mesenchymal stem cells (MSCs) and astrocyte, and were more sensitive to gefitinib and roscovitine. Among GSs, three hub genes KRT8, NGFR, and TCEA3, were screened and validated to potentially play feasible oncogenic roles in GBM.ConclusionConstruction of lncRNAs risk model and identification of GBM subtypes based on 17 irlncRNAs, which suggesting that irlncRNAs had the promising potential for clinical immunotherapy of GBM.

Project description:BackgroundDiffuse large B cell lymphoma (DLBCL) is the most common lymphoma in adults. Metabolic reprogramming in tumors is closely related to the immune microenvironment. This study aimed to explore the interactions between metabolism-associated genes (MAGs) and DLBCL prognosis and their potential associations with the immune microenvironment.MethodsGene expression and clinical data on DLBCL patients were obtained from the GEO database. Metabolism-associated molecular subtypes were identified by consensus clustering. A prognostic risk model containing 14 MAGs was established using Lasso-Cox regression in the GEO training cohort. It was then validated in the GEO internal testing cohort and TCGA external validation cohort. GO, KEGG and GSVA were used to explore the differences in enriched pathways between high- and low-risk groups. ESTIMATE, CIBERSORT, and ssGSEA analyses were used to assess the immune microenvironment. Finally, WGCNA analysis was used to identify two hub genes among the 14 model MAGs, and they were preliminarily verified in our tissue microarray (TMA) using multiple fluorescence immunohistochemistry (mIHC).ResultsConsensus clustering divided DLBCL patients into two metabolic subtypes with significant differences in prognosis and the immune microenvironment. Poor prognosis was associated with an immunosuppressive microenvironment. A prognostic risk model was constructed based on 14 MAGs and it was used to classify the patients into two risk groups; the high-risk group had poorer prognosis and an immunosuppressive microenvironment characterized by low immune score, low immune status, high abundance of immunosuppressive cells, and high expression of immune checkpoints. Cox regression, ROC curve analysis, and a nomogram indicated that the risk model was an independent prognostic factor and had a better prognostic value than the International Prognostic Index (IPI) score. The risk model underwent multiple validations and the verification of the two hub genes in TMA indicated consistent results with the bioinformatics analyses.ConclusionsThe molecular subtypes and a risk model based on MAGs proposed in our study are both promising prognostic classifications in DLBCL, which may provide novel insights for developing accurate targeted cancer therapies.

Dataset Information

Identification of a molecular signature of prognostic subtypes in diffuse-type gastric cancer.

Publications

Identification of a molecular signature of prognostic subtypes in diffuse-type gastric cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets