Dataset Information

Identification of the RP11-21C4.1/SVEP1 gene pair associated with FAT2 mutations as a potential biomarker in gastric cancer.

ABSTRACT: Gastric cancer (GC) is one of the most common malignancies worldwide. Despite rapid advances in systemic therapy, GC remains the third leading cause of cancer-related deaths. We aimed to identify a novel prognostic signature associated with FAT2 mutations in GC. We analyzed the expression levels of FAT2-mutant and FAT2-wildtype GC samples obtained from The Cancer Genome Atlas (TCGA). The Kaplan-Meier survival curve showed that patients with FAT2 mutations showed better prognosis than those without the mutation. Sixteen long non-coding RNAs (lncRNAs) and 62 messenger RNAs (mRNAs) associated with FAT2 mutations were correlated with the prognosis of GC. We then constructed a 4-mRNA signature and a 5-lncRNA signature for GC. Finally, we identified the most relevant RP11-21 C4.1/SVEP1 gene pair as a prognostic signature of GC that exhibited superior predictive performance in comparison with the 4-mRNA or 5-lncRNA signature by weighted gene correlation network analysis (WGCNA) and Cox proportional hazards regression analysis. In this study, we constructed a prognostic signature of GC by integrative genomics analysis, which also provided insights into the molecular mechanisms linked to FAT2 mutations in GC.

SUBMITTER: Zhou L

PROVIDER: S-EPMC8806586 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of the RP11-21C4.1/SVEP1 gene pair associated with FAT2 mutations as a potential biomarker in gastric cancer.

Zhou Lingshan L Yang Yuan Y Liu Min M Gan Yuling Y Liu Rong R Ren Man M Zheng Ya Y Wang Yuping Y Zhou Yongning Y

Bioengineered 20211201 1

Gastric cancer (GC) is one of the most common malignancies worldwide. Despite rapid advances in systemic therapy, GC remains the third leading cause of cancer-related deaths. We aimed to identify a novel prognostic signature associated with FAT2 mutations in GC. We analyzed the expression levels of FAT2-mutant and FAT2-wildtype GC samples obtained from The Cancer Genome Atlas (TCGA). The Kaplan-Meier survival curve showed that patients with FAT2 mutations showed better prognosis than those witho ...[more]

PMID: 34308747

Similar Datasets

Project description:BACKGROUND:The Top Scoring Pair (TSP) classifier, based on the concept of relative ranking reversals in the expressions of pairs of genes, has been proposed as a simple, accurate, and easily interpretable decision rule for classification and class prediction of gene expression profiles. The idea that differences in gene expression ranking are associated with presence or absence of disease is compelling and has strong biological plausibility. Nevertheless, the TSP formulation ignores significant available information which can improve classification accuracy and is vulnerable to selecting genes which do not have differential expression in the two conditions ("pivot" genes). RESULTS:We introduce the AUCTSP classifier as an alternative rank-based estimator of the magnitude of the ranking reversals involved in the original TSP. The proposed estimator is based on the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) and as such, takes into account the separation of the entire distribution of gene expression levels in gene pairs under the conditions considered, as opposed to comparing gene rankings within individual subjects as in the original TSP formulation. Through extensive simulations and case studies involving classification in ovarian, leukemia, colon, breast and prostate cancers and diffuse large b-cell lymphoma, we show the superiority of the proposed approach in terms of improving classification accuracy, avoiding overfitting and being less prone to selecting non-informative (pivot) genes. CONCLUSIONS:The proposed AUCTSP is a simple yet reliable and robust rank-based classifier for gene expression classification. While the AUCTSP works by the same principle as TSP, its ability to determine the top scoring gene pair based on the relative rankings of two marker genes across all subjects as opposed to each individual subject results in significant performance gains in classification accuracy. In addition, the proposed method tends to avoid selection of non-informative (pivot) genes as members of the top-scoring pair.

Dataset Information

Identification of the RP11-21C4.1/SVEP1 gene pair associated with FAT2 mutations as a potential biomarker in gastric cancer.

Publications

Identification of the RP11-21C4.1/SVEP1 gene pair associated with FAT2 mutations as a potential biomarker in gastric cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets