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Identification and external validation of a novel miRNA signature for lymph node metastasis prediction in submucosal-invasive gastric cancer patients.

ABSTRACT: Endoscopic resection (ER) has been increasingly performed in the treatment of early gastric cancer (GC). However, lymph node metastasis (LNM) can cause treatment failure with ER, especially in T1b patients. Here, we attempted to develop a miRNA-based classifier to detect LNM in T1b patients. Based on high-throughput data from The Cancer Genome Atlas, we identified 20 miRNAs whose expression significantly changed in T1-2 GC with LNM vs T1-2 GC without LNM. We then developed a miRNA signature to predict LNM of T1b GC using the LASSO model and backward step wise elimination approach in a training cohort. Furthermore, the predictive accuracy of this classifier was validated in both an internal testing group of 63 patients and an external independent group of 114 patients. This systematic and comprehensive in silico study identified a 7-miRNA signature with an area under the receiver operating characteristic curve (AUROC) value of 0.843 in T1-2 GC and 0.911 in T1 EGC. The backward elimination was further used to develop a 4-miRNA (miR-153-3p, miR-708, miR-940 and miR-375) risk-stratification model in the training cohort with an AUROC value of 0.898 in cohort 2. When pathologic results were used as a reference, the risk model yielded AUROC values of 0.829 and 0.792 in two cohorts of endoscopic biopsy specimens. This novel miRNA-LNM classifier works better than the currently used pathologic criteria of ER in T1b EGC. This classifier could individualize the management of T1b patients and facilitate treatment decisions.

SUBMITTER: Ma M

PROVIDER: S-EPMC6797584 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Identification and external validation of a novel miRNA signature for lymph node metastasis prediction in submucosal-invasive gastric cancer patients.

Ma Mingzhe M Lu Shixun S Liu Yinhua Y Kong Pengfei P Long Ziwen Z Wan Ping P Zhang Yan Y Wang Yanong Y Xu Dazhi D

Cancer medicine 20190904 14

Endoscopic resection (ER) has been increasingly performed in the treatment of early gastric cancer (GC). However, lymph node metastasis (LNM) can cause treatment failure with ER, especially in T1b patients. Here, we attempted to develop a miRNA-based classifier to detect LNM in T1b patients. Based on high-throughput data from The Cancer Genome Atlas, we identified 20 miRNAs whose expression significantly changed in T1-2 GC with LNM vs T1-2 GC without LNM. We then developed a miRNA signature to p ...[more]

PMID: 31486298

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Project description:AimsRecent studies have shown that the microenvironment can include cancer cells and cancer-associated fibroblasts (CAFs), and that both play important roles in the progression and metastasis of CRC. Here, we aimed to analyse the expression patterns of cancer cell- and CAF-related proteins in submucosal invasive colorectal cancer (SiCRC) and whether such markers are correlated with lymph node metastasis (LNM).Methods and resultsQuantitative analysis was conducted for Ki-67, p53, β-catenin and matrix metalloproteinase-7 (MMP7) to assess cancer cell markers. In addition, we examined CAF markers, including smooth muscle alpha-actin (α-SMA), CD10, podoplanin, fibroblast-specific protein 1 (FSP-1), platelet-derived growth factor receptor (PDGFR)-α, PDGFR-β, adipocyte enhancer-binding protein 1 (AEBP1), fibroblast-associated protein 1 (FAP-1), zinc finger E-box binding homeobox 1 (ZEB1) and TWIST-related protein 1 (TWIST1). In both cases, we conducted digital pathology with Aperio software. We also examined the expression patterns of biomarkers using hierarchical cluster analysis. Two subgroups were established based on the expression patterns of cancer cell- and CAF- related markers, and the associations of these subgroups with clinicopathological variables. In multivariate analysis, subgroup 2, which was characterised by high expression of Ki-67, p53, FAP-1, platelet-derived growth factor receptor (PDGFR)-α, PDGFR-β and TWIST1, was correlated with LNM (P < 0.01). Next, we examined the associations of individual biomarkers with LNM. Multivariate analysis showed that high expression levels of Ki-67 and FAP-1 were significantly associated with LNM (P < 0.05).ConclusionsOur findings showed that expression patterns of cancer cell- and CAF-related proteins may allow for stratification of patients into risk categories for LNM in SiCRC. In addition, Ki-67- and FAP-1-expressing microenvironmental cells might be helpful for identification of correlations with LNM in SiCRC.

Project description:BackgroundBecause its metastasis to the lymph nodes are closely related to poor prognosis, miRNAs and mRNAs can serve as biomarkers for the diagnosis, prognosis, and therapy of colorectal cancer (CRC). This study aimed to identify novel gene signatures in the lymph node metastasis of CRC.MethodsGSE56350, GSE70574, and GSE95109 datasets were downloaded from the Gene Expression Omnibus (GEO) database, while data from 569 colorectal cancer cases were also downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed miRNAs (DE-miRNAs) were calculated using R programming language (Version 3.6.3), while gene ontology and enrichment analysis of target mRNAs were performed using FunRich ( http://www.funrich.org ). Furthermore, the mRNA-miRNA network was constructed using Cytoscape software (Version 3.8.0). Gene expression levels were verified using the GEO datasets. Similarly, quantitative real-time PCR (qPCR) was used to examine expression profiles from 20 paired non-metastatic and metastatic lymph node tissue samples obtained from patients with CRC.ResultsIn total, five DE-miRNAs were selected, and 34 mRNAs were identified after filtering the results. Moreover, two key miRNAs (hsa-miR-99a, hsa-miR-100) and one gene (heparan sulfate-glucosamine 3-sulfotransferase 2 [HS3ST2]) were identified. The GEO datasets analysis and qPCR results showed that the expression of key miRNA and genes were consistent with that obtained from the bioinformatic analysis. A novel miRNA-mRNA network capable of predicting the prognosis and confirmed experimentally, hsa-miR-99a-HS3ST2-hsa-miR-100, was found after expression analysis in metastasized lymph node tissue from CRC samples.ConclusionIn summary, miRNAs and genes with potential as biomarkers were found and a novel miRNA-mRNA network was established for CRC lymph node metastasis by systematic bioinformatic analysis and experimental validation. This network may be used as a potential biomarker in the development of lymph node metastatic CRC.

Project description:BackgroundmiRNAs and genes can serve as biomarkers for the prognosis and therapy of cervical tumors whose metastasis into lymph nodes is closely associated with disease progression and poor prognosis.MethodsR software and Bioconductor packages were employed to identify differentially expressed miRNAs (DEMs) from The Cancer Genome Atlas (TCGA) database. GEO2R detected differentially expressed genes (DEGs) in the GSE7410 dataset originating from the Gene Expression Omnibus (GEO). A Cox proportional hazard regression model was established to select prognostic miRNA biomarkers. Online tools such as TargetScan and miRDB predicted target genes, and overlapping DEGs and target genes were defined as consensus genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and Gene Ontology (GO) function annotations were performed to discern the potential functions of consensus genes. STRING and Cytoscape screened key genes and constructed a regulatory network.ResultsA combination of four miRNAs (down-regulated miR-502 and miR-145, up-regulated miR-142 and miR-33b) was identified as an independent prognostic signature of cervical cancer. A total of 94 consensus genes were significantly enriched in 7 KEGG pathways and 19 GO function annotations including the cAMP signaling pathway, the plasma membrane, integral components of the plasma membrane, cell adhesion, etc. The module analysis suggested that CXCL12, IGF1, PTPRC CDH5, RAD51B, REV3L, and WDHD1 are key genes that significantly correlate with cervical cancer lymph node metastasis.ConclusionsThis study demonstrates that a four-miRNA signature can be a prognostic biomarker, and seven key genes are significantly associated with lymph node metastasis in cervical cancer patients. These miRNAs and key genes have the potential to be therapeutic targets for cervical cancer. Among them, two miRNAs (miR-502 and miR-33b) and two key genes (PTPRC and CDH5) were first reported to be potential novel biomarkers for cervical cancer. The current study further characterizes the progression of lymph node metastasis and mechanism of cervical tumors; therefore, it provides a novel diagnostic indicator and therapeutic targets for future clinical treatments.

Project description:BackgroundLymph node metastasis (LNM) is an important factor for both treatment and prognosis of early gastric cancer (EGC). Current methods are insufficient to evaluate LNM in EGC due to suboptimal accuracy. Herein, we aim to identify methylation signatures for LNM of EGC, facilitate precision diagnosis, and guide treatment modalities.MethodsFor marker discovery, genome-wide methylation sequencing was performed in a cohort (marker discovery) using 47 fresh frozen (FF) tissue samples. The identified signatures were subsequently characterized for model development using formalin-fixed paraffin-embedded (FFPE) samples by qPCR assay in a second cohort (model development cohort, n = 302, training set: n = 151, test set: n = 151). The performance of the established model was further validated using FFPE samples in a third cohorts (validation cohort, n = 130) and compared with image-based diagnostics, conventional clinicopathology-based model (conventional model), and current standard workups.ResultsFifty LNM-specific methylation signatures were identified de novo and technically validated. A derived 3-marker methylation model for LNM diagnosis was established that achieved an AUC of 0.87 and 0.88, corresponding to the specificity of 80.9% and 85.7%, sensitivity of 80.6% and 78.1%, and accuracy of 80.8% and 83.8% in the test set of model development cohort and validation cohort, respectively. Notably, this methylation model outperformed computed tomography (CT)-based imaging with a superior AUC (0.88 vs. 0.57, p < 0.0001) and individual clinicopathological features in the validation cohort. The model integrated with clinicopathological features demonstrated further enhanced AUCs of 0.89 in the same cohort. The 3-marker methylation model and integrated model reduced 39.4% and 41.5% overtreatment as compared to standard workups, respectively.ConclusionsA novel 3-marker methylation model was established and validated that shows diagnostic potential to identify LNM in EGC patients and thus reduce unnecessary gastrectomy in EGC.

Dataset Information

Identification and external validation of a novel miRNA signature for lymph node metastasis prediction in submucosal-invasive gastric cancer patients.

Publications

Identification and external validation of a novel miRNA signature for lymph node metastasis prediction in submucosal-invasive gastric cancer patients.

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