Dataset Information

An Integrated Bioinformatic Analysis of the S100 Gene Family for the Prognosis of Colorectal Cancer.

ABSTRACT: Background: S100 family genes exclusively encode at least 20 calcium-binding proteins, which possess a wide spectrum of intracellular and extracellular functions in vertebrates. Multiple lines of evidences suggest that dysregulated S100 proteins are associated with human malignancies including colorectal cancer (CRC). However, the diverse expression patterns and prognostic roles of distinct S100 genes in CRC have not been fully elucidated.

Methods: In the current study, we analyzed the mRNA expression levels of S100 family genes and proteins and their associations with the survival of CRC patients using the Oncomine analysis and GEPIA databases. Expressions and mutations of S100 family genes were analyzed using the cBioPortal, and protein-protein interaction (PPI) networks of S100 proteins and their mutation-related coexpressed genes were analyzed using STRING and Cytoscape.

Results: We observed that the mRNA expression levels of S100A2, S100A3, S100A9, S100A11, and S100P were higher and the level of S100B was lower in CRC tissues than those in normal colon mucosa. A high S100A10 levels was associated with advanced-stage CRC. Results from GEPIA database showed that highly expressed S100A1 was correlated with worse overall survival (OS) and disease-free survival (DFS) and that overexpressions of S100A2 and S100A11 were associated with poor DFS of CRC, indicating that S100A1, S100A2, and S100A11 are potential prognostic markers. Unexpectedly, most of S100 family genes showed no significant prognostic values in CRC.

Conclusions: Our findings, though still need to be ascertained, offer novel insights into the prognostic implications of the S100 family in CRC and will inspire more clinical trials to explore potential S100-targeted inhibitors for the treatment of CRC.

SUBMITTER: Zeng ML

PROVIDER: S-EPMC7718059 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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An Integrated Bioinformatic Analysis of the S100 Gene Family for the Prognosis of Colorectal Cancer.

Zeng Meng-Lu ML Zhu Xian-Jin XJ Liu Jin J Shi Peng-Chong PC Kang Yan-Li YL Lin Zhen Z Cao Ying-Ping YP

BioMed research international 20201126

<h4>Background</h4>S100 family genes exclusively encode at least 20 calcium-binding proteins, which possess a wide spectrum of intracellular and extracellular functions in vertebrates. Multiple lines of evidences suggest that dysregulated S100 proteins are associated with human malignancies including colorectal cancer (CRC). However, the diverse expression patterns and prognostic roles of distinct S100 genes in CRC have not been fully elucidated.<h4>Methods</h4>In the current study, we analyzed ...[more]

PMID: 33294444

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Project description:Background: Colorectal cancer (CRC) is one gastrointestinal malignancy, accounting for 10% of cancer diagnoses and cancer-related deaths worldwide each year. Therefore, it is urgent to identify genes involved in CRC predicting the prognosis. Methods: CRC's data were acquired from the Gene Expression Omnibus (GEO) database (GSE39582 and GSE41258 datasets) and The Cancer Genome Atlas (TCGA) database. The differentially expressed necroptosis-related genes (DENRGs) were sorted out between tumor and normal tissues. Univariate Cox regression analysis and least absolute shrinkage and selectionator operator (LASSO) analysis were applied to selected DENRGs concerning patients' overall survival and to construct a prognostic biomarker. The effectiveness of this biomarker was assessed by the Kaplan-Meier curve and the receiver operating characteristic (ROC) analysis. The GSE39582 dataset was utilized as external validation for the prognostic signature. Moreover, using univariate and multivariate Cox regression analyses, independent prognostic factors were identified to construct a prognostic nomogram. Next, signaling pathways regulated by the signature were explored through the gene set enrichment analysis (GSEA). The single sample gene set enrichment analysis (ssGSEA) algorithm and tumor immune dysfunction and exclusion (TIDE) were used to explore immune correlation in the two groups, high-risk and low-risk ones. Finally, prognostic genes' expression was examined in the GSE41258 dataset. Results: In total, 27 DENRGs were filtered, and a necroptosis-related prognostic signature based on 6 DENRGs was constructed, which may better understand the overall survival (OS) of CRC. The Kaplan-Meier curve manifested the effectiveness of the prognostic signature, and the ROC curve showed the same result. In addition, univariate and multivariate Cox regression analyses revealed that age, pathology T, and risk score were independent prognostic factors, and a nomogram was established. Furthermore, the prognostic signature was most significantly associated with the apoptosis pathway. Meanwhile, 24 immune cells represented significant differences between two groups, like the activated B cell. Furthermore, 32 immune checkpoints, TIDE scores, PD-L1 scores, and T-cell exclusion scores were significantly different between the two groups. Finally, a 6-gene prognostic signature represented different expression levels between tumor and normal samples significantly in the GSE41258 dataset. Conclusion: Our study established a signature including 6 genes and a prognostic nomogram that could significantly assess the prognosis of patients with CRC.

Project description:Colorectal cancer (CRC) is a life-threatening disease with a poor prognosis. Therefore, it is crucial to identify molecular prognostic biomarkers for CRC. The present study aimed to identify potential key genes that could be used to predict the prognosis of patients with CRC. Three CRC microarray datasets (GSE20916, GSE73360 and GSE44861) were downloaded from the Gene Expression Omnibus (GEO) database, and one dataset was obtained from The Cancer Genome Atlas (TCGA) database. The three GEO datasets were analyzed to detect differentially expressed genes (DEGs) using the BRB-ArrayTools software. Functional and pathway enrichment analyses of these DEGs were performed using the Database for Annotation, Visualization and Integrated Discovery tool. A protein-protein interaction (PPI) network of DEGs was constructed, hub genes were extracted, and modules of the PPI network were analyzed. To investigate the prognostic values of the hub genes in CRC, data from the CRC datasets of TCGA were used to perform the survival analyses based on the sample splitting method and Cox regression model. Correlation among the hub genes was evaluated using Spearman's correlation analysis. In the three GEO datasets, a total of 105 common DEGs were identified, including 51 down- and 54 up-regulated genes in CRC compared with normal colorectal tissues. A PPI network consisting of 100 DEGs and 551 edges was constructed, and 44 nodes were identified as hub genes. Among these 44 genes, the four hub genes TIMP metallopeptidase inhibitor 1 (TIMP1), solute carrier family 4 member 4 (SLC4A4), aldo-keto reductase family 1 member B10 (AKR1B10) and ATP binding cassette subfamily E member 1 (ABCE1) were associated with overall survival (OS) in patients with CRC. Three significant modules were extracted from the PPI network. The hub gene TIMP1 was present in Module 1, ABCE1 was involved in Module 2 and SLC4A4 was identified in Module 3. Univariate analysis revealed that TIMP1, SLC4A4, AKR1B10 and ABCE1 were associated with the OS of patients with CRC. Multivariate analysis demonstrated that SLC4A4 may be an independent prognostic factor associated with OS. Furthermore, the results from correlation analysis revealed that there was no correlation between TIMP1, SLC4A4 and ABCE1, whereas AKR1B10 was positively correlated with SLC4A4. In conclusion, the four key genes TIMP1, SLC4A4, AKR1B10 and ABCE1 associated with the OS of patients with CRC were identified by integrated bioinformatics analysis. These key genes may be used as prognostic biomarkers to predict the survival of patients with CRC, and may therefore represent novel therapeutic targets for CRC.

Project description:BackgroundColorectal cancer (CRC) is the third most lethal and malignant type of cancer in the world. Abnormal expression of human microRNA-200a (hsa-miRNA-200a or miR-200a) has previously been characterized as a clinically noticeable biomarker in several cancers, but its role in CRC is still unclear.MethodsThree CRC miRNA expression datasets were integratively analyzed by Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithms. Nine candidate miRNAs were identified and validated for diagnostic and prognostic capability with the prediction model. The potential roles of the tumor suppressor miR-200a-3p in invasion, migration, and epithelial-mesenchymal transition of CRC cells were elaborated by in vitro studies.ResultsNine miRNAs (miR-492, miR-200a, miR-338, miR-29c, miR-101, miR-148a, miR-92a, miR-424, and miR-210) were identified as potentially useful diagnostic biomarkers in the clinic. The overall accuracy rate of the nine miRNAs in the diagnostic model was 0.94, 0.89, and 0.978 in the testing, validation, and independent validation dataset, respectively. CRC patients in the GSE29622 cohort were separated by the prognostic model into the low-risk score group and the high-risk score group. The area under the receiver operating characteristic curve (AUC) was 0.872 and 0.783 for predicting the 1- to 10-year survival of CRC patients. The performance of the prognostic model was validated by an independent TCGA-Colon Adenocarcinoma (COAD) dataset with AUC values between 0.911 and 0.796 in predicting 1- to 10-year survival. Nomograms comprising risk scores, tumor stage, and TNM staging were generated for predicting 1-, 3-, and 5-year overall survival (OS) in the GSE29622 and TCGA-COAD datasets. Colony formation, invasion, and migration in DLD1 and SW480 cells were suppressed by overexpression of miR-200a-3p. Inhibition of miR-200a-3p function contributed to abnormal colony formation, migration, invasion, and epithelial-mesenchymal transition (EMT). miR-200a-3p binding sites were located within the 3'-untranslated region (3'-UTR) of the Forkhead box protein A1 (FOXA1) mRNA.ConclusionWe developed and validated a diagnostic and prognostic prediction model for CRC. miR-200a-3p was determined to be a potential diagnostic and prognostic biomarker for CRC.

Dataset Information

An Integrated Bioinformatic Analysis of the S100 Gene Family for the Prognosis of Colorectal Cancer.

Publications

An Integrated Bioinformatic Analysis of the S100 Gene Family for the Prognosis of Colorectal Cancer.

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