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DNA computing for gastric cancer analysis and functional classification.

ABSTRACT: Early identification of key biomarkers of malignant cancer is vital for patients' prognosis and therapies. There is research demonstrating that microRNAs are important biomarkers for cancer analysis. In this article, we used the DNA strand displacement mechanism (DSD) to construct the DNA computing system for cancer analysis. First, gene chips were obtained through bioinformatical training. These microRNA data and clinical traits were obtained from the Cancer Genome Atlas (TCGA) dataset. Second, we analyzed the expression data by using a weighted gene co-expression network (WGCNA) and found four biomarkers for two clinic features, respectively. Last, we constructed a DSD-based DNA computing system for cancer analysis. The inputs of the system are these identified biomarkers; the outputs are the fluorescent signals that represent their corresponding traits. The experiment and simulation results demonstrated the reliability of the DNA computing system. This DSD simulation system is lab-free but clinically meaningful. We expect this innovative method to be useful for rapid and accurate cancer diagnosis.

SUBMITTER: Chen C

PROVIDER: S-EPMC9729876 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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DNA computing for gastric cancer analysis and functional classification.

Chen Congzhou C Chen Xin X Li Xin X Shi Xiaolong X

Frontiers in genetics 20221124

Early identification of key biomarkers of malignant cancer is vital for patients' prognosis and therapies. There is research demonstrating that microRNAs are important biomarkers for cancer analysis. In this article, we used the DNA strand displacement mechanism (DSD) to construct the DNA computing system for cancer analysis. First, gene chips were obtained through bioinformatical training. These microRNA data and clinical traits were obtained from the Cancer Genome Atlas (TCGA) dataset. Second, ...[more]

PMID: 36506309

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Project description:BackgroundPreoperative prediction of the Lauren classification in gastric cancer (GC) is very important to the choice of therapy, the evaluation of prognosis, and the improvement of quality of life. However, there is not yet radiomics analysis concerning the prediction of Lauren classification straightly. In this study, a radiomic nomogram was developed to preoperatively differentiate Lauren diffuse type from intestinal type in GC.MethodsA total of 539 GC patients were enrolled in this study and later randomly allocated to two cohorts at a 7:3 ratio for training and validation. Two sets of radiomic features were derived from tumor regions and peritumor regions on venous phase computed tomography (CT) images, respectively. With the least absolute shrinkage and selection operator logistic regression, a combined radiomic signature was constructed. Also, a tumor-based model and a peripheral ring-based model were built for comparison. Afterwards, a radiomic nomogram integrating the combined radiomic signature and clinical characteristics was developed. All the models were evaluated regarding classification ability and clinical usefulness.ResultsThe combined radiomic signature achieved an area under receiver operating characteristic curve (AUC) of 0.715 (95% confidence interval [CI], 0.663-0.767) in the training cohort and 0.714 (95% CI, 0.636-0.792) in the validation cohort. The radiomic nomogram incorporating the combined radiomic signature, age, CT T stage, and CT N stage outperformed the other models with a training AUC of 0.745 (95% CI, 0.696-0.795) and a validation AUC of 0.758 (95% CI, 0.685-0.831). The significantly improved sensitivity of radiomic nomogram (0.765 and 0.793) indicated better identification of diffuse type GC patients. Further, calibration curves and decision curves demonstrated its great model fitness and clinical usefulness.ConclusionsThe radiomic nomogram involving the combined radiomic signature and clinical characteristics holds potential in differentiating Lauren diffuse type from intestinal type for reasonable clinical treatment strategy.

Project description:ObjectiveTo explore the expression characteristics of ELK3 and its role in the occurrence, progression and prognosis of gastric cancer.MethodsWe analyzed the expression characteristics of ELK3 in gastric cancer based on E-MTAB-6693 dataset and explored the prognostic value of ELK3 using Kaplan-Meier survival analysis and univariate and multivariate Cox regression analysis. Chip-Atlas, ChipBase, Genes Transcription Regulation Database, and hTFtarget were used for predicting the target genes of ELK3 and constructing the transcription regulation network. Functional enrichment analysis of the target genes was performed using R software. The proportions of infiltrating immune cells in gastric cancer were analyzed using Cibersort tool, and the Pearson coefficients between ELK3 and these cells were calculated. The expression profile of ELK3 was verified based on Gene Expression Profiling Interactive Analysis and Human Protein Atlas databases. We also collected 5 pairs of gastric cancer and adjacent tissue samples and detected the expression of ELK3 at both the mRNA and protein levels using RT-PCR and Western blotting.ResultsIn public datasets and clinical samples, ELK3 was highly expressed in gastric cancer (P < 0.05), and its expression increased with the progression of M stage, AJCC stage, and perineural invasion (P < 0.05). ELK3 expression was correlated with N stage, AJCC stage, Lauren classification, differentiation, pathological classification, and microsatellite status of gastric cancer (P < 0.05). A high expression of ELK3 was associated with significantly reduced overall survival and disease-free survival of the patients, and served as an independent prognostic factor of gastric cancer (P < 0.05). Comprehensive analysis identified 176 potential target genes of ELK3, and enrichment analysis showed that ELK3 may regulate Rap1, AMPK, chemokines, VEGF, TNF, and tumor PD-L1/PD-1 signaling (PP < 0.05). The expression of ELK3 was negatively correlated with regulatory T cells, follicular helper T cells, and CD8+T cells in gastric cancer (P < 0.05).ConclusionELK3 acts as an oncogene in gastric cancer, and its high expression may promote the occurrence, progression and immune escape of gastric cancer.

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DNA computing for gastric cancer analysis and functional classification.

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DNA computing for gastric cancer analysis and functional classification.

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