Dataset Information

Development and clinical validation of a 3-miRNA signature to predict prognosis of gastric cancer.

ABSTRACT:

Aims

Identification of miRNA signature to predict the prognosis of gastric cancer (GC) patients by integrating bioinformatics and experimental validation.

Methods

The miRNA expression profile and clinical data of GC were collected. The univariable and LASSO-Cox regression were used to construct the risk signature. The receiver operating characteristic (ROC) curve analysis confirmed the good performance of the prognostic model.

Results

A 3-miRNA prognostic signature was constructed, which included hsa-miR-126-3p, hsa-miR-143-5p, and hsa-miR-1275. A nomogram, including the prognostic signature to predict the overall survival, was established, and internal validation in the The Cancer Genome Atlas (TCGA) cohort was performed. We found that compared with the traditional pathological stage, the nomogram was the best at predicting the prognosis.

Conclusions

The predictive model and the nomogram will enable patients with GC to be more accurately managed in clinical practice.

SUBMITTER: Qi W

PROVIDER: S-EPMC7866890 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development and clinical validation of a 3-miRNA signature to predict prognosis of gastric cancer.

Qi Wenqian W Zhang Qian Q

PeerJ 20210203

<h4>Aims</h4>Identification of miRNA signature to predict the prognosis of gastric cancer (GC) patients by integrating bioinformatics and experimental validation.<h4>Methods</h4>The miRNA expression profile and clinical data of GC were collected. The univariable and LASSO-Cox regression were used to construct the risk signature. The receiver operating characteristic (ROC) curve analysis confirmed the good performance of the prognostic model.<h4>Results</h4>A 3-miRNA prognostic signature was cons ...[more]

PMID: 33604158

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Project description:Background: Gastric cancer (GC) is the fifth most common malignancy and the third leading cause of tumor-related deaths globally. Herein, we attempted to build a novel immune-related gene (IRG) signature that could predict the prognosis and immunotherapeutic efficiency for GC patients. Methods: The mRNA transcription data and corresponding clinical data of GC were downloaded from The Cancer Genome Atlas (TCGA) database as the training group and the GSE84437 data set as the testing cohort, followed by acquisition of IRGs from the InnateDB resource and ImmPort database. Using the univariate Cox regression analysis, an IRG signature was developed. Several immunogenomic analyses were performed to illustrate the associations between the immune risk score and tumor mutational burden, immune cell infiltrations, function of immune infiltration, clinical characteristics, immune subtype, and immunotherapeutic response. Results: The analysis of 343 GC samples and 30 normal samples from the TCGA database gave rise to 8,713 differentially expressed genes (DEGs) and 513 differentially expressed immune-related genes (DEIRGs) were extracted. The novel IRG signature contained eight DEIRGs (FABP4, PI15, RNASE2, CGB5, INHBE, RLN2, DUSP1, and CD36) and was found to serve as an independent predictive and prognostic factor for GC. Then, the GC patients were separated into the high- and low-risk groups based on the median risk score, wherein the low-risk group presented a better prognosis and was more sensitive to immunotherapy than did the high-risk group. According to the time-dependent ROC curves and AUCs, the immunotherapeutic value of the signature was better than the Tumor Immune Dysfunction and Exclusion (TIDE) and T-cell inflammatory signature (TIS) scores. In addition, the AUCs of the risk score for predicting 1-, 2-, and 3-year OS were 0.675, 0.682, and 0.710, respectively, which indicated that the signature had great predictive power. Conclusion: This study presents a novel IRG signature based on the tumor immune microenvironment, which could improve the prediction of the prognosis and immunotherapeutic efficiency for GC patients. The powerful signature may serve as novel biomarkers and provide therapeutic targets for precision oncology in clinical practice.

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Dataset Information

Development and clinical validation of a 3-miRNA signature to predict prognosis of gastric cancer.

Aims

Methods

Results

Conclusions

Publications

Development and clinical validation of a 3-miRNA signature to predict prognosis of gastric cancer.

Similar Datasets

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