Identification of Novel Prognostic Biomarkers for Colorectal Cancer by Bioinformatics Analysis
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PROVIDER: S-EPMC10837579 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:Liver cancer is a common malignant tumor with poor prognosis. Due to the lack of specific clinical manifestations at early stages, most patients are already at advanced stages of the disease by the time of diagnosis. Identification of novel biomarkers for liver cancer may thus enable earlier detection, improving outcome. MicroRNAs (miRNAs) are small endogenous noncoding RNAs of 18-22 nucleotides in length, which have a regulatory role in the expression of target proteins. Increased evidence suggests that miRNAs are abnormally expressed in a variety of cancer malignancies. Here, we combined RNA sequencing data and clinical information from The Cancer Genome Atlas Liver Hepatocellular Carcinoma database for weighted gene coexpression network analysis to identify potential miRNA prognostic biomarkers. We constructed nine coexpression modules, allowing us to identify that miR-105-5p, miR-767-5p, miR-1266-5p, miR-4746-5p, miR-500a-3p, miR-1180-3p and miR-139-5p are significantly associated with liver cancer prognosis. We found that these miRNAs exhibit significant association with prognosis of patients with liver cancer and confirmed the expression of these miRNAs in liver cancer tissues. Multivariate Cox regression analysis showed that miR-105-5p and miR-139-5p may be considered as independent factors. In summary, here we report that seven miRNAs have potential value as prognostic biomarkers of liver cancer.
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Project description:BackgroundColorectal cancer (CRC) is one of the most common malignant tumors. In the present study, the expression profile of human multistage colorectal mucosa tissues, including healthy, adenoma, and adenocarcinoma samples was downloaded to identify critical genes and potential drugs in CRC.MethodsExpression profiles, GSE33113 and GSE44076, were integrated using bioinformatics methods. Differentially expressed genes (DEGs) were analyzed by R language. Functional enrichment analyses of the DEGs were performed using the Database for Annotation, visualization, and integrated discovery (DAVID) database. Then, the search tool for the retrieval of interacting genes (STRING) database and Cytoscape were used to construct a protein-protein interaction (PPI) network and identify hub genes. Subsequently, survival analysis was performed among the key genes using Gene Expression Profiling Interactive Analysis (GEPIA). Connectivity Map (CMap) was used to query potential drugs for CRC.ResultsA total of 428 upregulated genes and 751 downregulated genes in CRC were identified. The functional changes of these DEGs were mainly associated with cell cycle, oocyte meiosis, DNA replication, p53 signaling pathway, and progesterone-mediated oocyte maturation. A PPI network was identified by STRING with 482 nodes and 2,368 edges. Survival analysis revealed that high mRNA expression of AURKA, CCNB1, CCNF, and EXO1 was significantly associated with longer overall survival. Moreover, CMap predicted a panel of small molecules as possible adjuvant drugs to treat CRC.ConclusionOur study found key dysregulated genes involved in CRC and potential drugs to combat it, which may provide novel insights and potential biomarkers for prognosis, as well as providing new CRC treatments.
| S-EPMC6625111 | biostudies-literature
Project description:Colorectal cancer (CRC) is one of the most deadly cancers in the world with few reliable biomarkers that have been selected into clinical guidelines for prognosis of CRC patients. In this study, mRNA microarray datasets GSE113513, GSE21510, GSE44076, and GSE32323 were obtained from the Gene Expression Omnibus (GEO) and analyzed with bioinformatics to identify hub genes in CRC development. Differentially expressed genes (DEGs) were analyzed using the GEO2R tool. Gene ontology (GO) and KEGG analyses were performed through the DAVID database. STRING database and Cytoscape software were used to construct a protein-protein interaction (PPI) network and identify key modules and hub genes. Survival analyses of the DEGs were performed on GEPIA database. The Connectivity Map database was used to screen potential drugs. A total of 865 DEGs were identified, including 374 upregulated and 491 downregulated genes. These DEGs were mainly associated with metabolic pathways, pathways in cancer, cell cycle and so on. The PPI network was identified with 863 nodes and 5817 edges. Survival analysis revealed that HMMR, PAICS, ETFDH, and SCG2 were significantly associated with overall survival of CRC patients. And blebbistatin and sulconazole were identified as candidate drugs. In conclusion, our study found four hub genes involved in CRC, which may provide novel potential biomarkers for CRC prognosis, and two potential candidate drugs for CRC.
| S-EPMC7874272 | biostudies-literature
Project description:BackgroundTumor progression and distant metastasis are the main causes of deaths in colorectal cancer (CRC) patients, and the molecular mechanisms in CRC metastasis have not been completely discovered.MethodsWe identified differentially expressed genes (DEGs) and lncRNAs (DELs) of CRC from The Cancer Genome Atlas (TCGA) database. Then we conducted the weighted gene co-expression network analysis (WGCNA) to investigate co-expression modules related with CRC metastasis. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, DEG-DEL co-expression network and survival analyses of significant modules were also conducted. Finally, the expressions of selected biomarkers were validated in cell lines by quantitative real-time PCR (qRT-PCR).Results2032 DEGs and 487 DELs were involved the construction of WGCNA network, and greenyellow, turquoise and brown module were identified to have more significant correlation with CRC metastasis. GO and KEGG pathway analysis of these three modules have proven that the functions of DEGs were closely involved in many important processes in cancer pathogenesis. Through the DEG-DEL co-expression network, 12 DEGs and 2 DELs were considered as hub nodes. Besides, survival analysis showed that 30 DEGs were associated with the overall survival of CRC. Then 10 candidate biomarkers were chosen for validation and the expression of CA2, CHP2, SULT1B1, MOGAT2 and C1orf115 were significantly decreased in CRC cell lines when compared to normal human colonic epithelial cells, which were consistent with the results of differential expression analysis. Especially, low expression of SULT1B1, MOGAT2 and C1orf115 were closely correlated with poorer survival of CRC.ConclusionThis study identified 5 genes as new biomarkers affecting the metastasis of CRC. Besides, SULT1B1, MOGAT2 and C1orf115 might be implicated in the prognosis of CRC patients.
| S-EPMC7106634 | biostudies-literature
Project description:PurposeThe prognosis of breast cancer (BC) patients who develop into brain metastases (BMs) is very poor. Thus, it is of great significance to explore the etiology of BMs in BC and identify the key genes involved in this process to improve the survival of BC patients with BMs.Patients and methodsThe gene expression data and the clinical information of BC patients were downloaded from TCGA and GEO database. Differentially expressed genes (DEGs) in TCGA-BRCA and GSE12276 were overlapped to find differentially expressed metastatic genes (DEMGs). The protein-protein interaction (PPI) network of DEMGs was constructed via STRING database. ClusterProfiler R package was applied to perform the gene ontology (GO) enrichment analysis of DEMGs. The univariate Cox regression analysis and the Kaplan-Meier (K-M) curves were plotted to screen DEMGs associated with the overall survival and the metastatic recurrence survival, which were identified as the key genes associated with the BMs in BC. The immune infiltration and the expressions of immune checkpoints for BC patients with brain relapses and BC patients with other relapses were analyzed respectively. The correlations among the expressions of key genes and the differently infiltrated immune cells or the differentially expressed immune checkpoints were calculated. The gene set enrichment analysis (GSEA) of each key gene was conducted to investigate the potential mechanisms of key genes involved in BC patients with BMs. Moreover, CTD database was used to predict the drug-gene interaction network of key genes.ResultsA total of 154 DEGs were identified in BC patients at M0 and M1 in TCGA database. A total of 667 DEGs were identified in BC patients with brain relapses and with other relapses. By overlapping these DEGs, 17 DEMGs were identified, which were enriched in the cell proliferation related biological processes and the immune related molecular functions. The univariate Cox regression analysis and the Kaplan-Meier curves revealed that CXCL9 and GPR171 were closely associated with the overall survival and the metastatic recurrence survival and were identified as key genes associated with BMs in BC. The analyses of immune infiltration and immune checkpoint expressions showed that there was a significant difference of the immune microenvironment between brain relapses and other relapses in BC. GSEA indicated that CXCL9 and GPR171 may regulate BMs in BC via the immune-related pathways.ConclusionOur study identified the key genes associated with BMs in BC patients and explore the underlying mechanisms involved in the etiology of BMs in BC. These findings may provide a promising approach for the treatments of BC patients with BMs.
| S-EPMC8760394 | biostudies-literature
Project description:BackgroundColorectal cancer (CRC), one of the most common malignant tumors worldwide, has a high mortality rate, especially for patients with CRC liver metastasis (CLM). However, CLM pathogenesis remains unclear.MethodsWe integrated multiple cohort datasets and databases to clarify and verify potential key candidate biomarkers and signal transduction pathways in CLM. GEO2R, DAVID 6.8, ImageGP, STRING, UALCAN, ONCOMINE, THE HUMAN PROTEIN ATLAS, GEPIA 2.0, cBioPortal, TIMER 2.0, DRUGSURV, CRN, GSEA 4.0.3, FUNRICH 3.1.3 and R 4.0.3 were utilized in this study.ResultsSixty-three pairs of matched colorectal primary cancer and liver metastatic gene expression profiles were screened from three gene expression profiles (GSE6988, GSE14297 and GSE81558). Thirty-one up-regulated genes and four down-regulated genes were identified from these three gene expression profiles and verified by another gene expression profiles (GSE 49355) and TCGA database. Two pathways (IGFBP-IGF signaling pathway and complement-coagulation cascade), eighteen key differentially expressed genes (DEGs), six hub genes (SPARCL1, CDH2, CP, HP, TF and SERPINA5) and two biomarkers (CDH2 and SPARCL1) with significantly prognostic values were screened by multi-omics data analysis and verified by Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) cohort.ConclusionsIn this study, we identified a robust set of potential candidate biomarkers in CLM, which would provide potential value for early diagnosis and prognosis, and would promote molecular targeting therapy for CRC and CLM.
| S-EPMC8371911 | biostudies-literature
Project description:BackgroundOur study aimed to investigate the Hub genes and their prognostic value in colorectal cancer (CRC) via bioinformatics analysis.MethodsThe data set of colorectal cancer was downloaded from the GEO database (GSE21510, GSE110224 and GSE74602) for differential expression analysis using the GEO2R tool. Hub genes were screened by protein-protein interaction (PPI) comprehensive analysis. GEPIA was used to verify the expression of Hub genes and evaluate its prognostic value. The protein expression of Hub gene in CRC was analyzed using the Human Protein Atlas database. The cBioPortal was used to analyze the type and frequency of Hub gene mutations, and the effects of mutation on the patients' prognosis. The TIMER database was used to study the correlation between Hub genes and immune infiltration in CRC. Gene set enrichment analysis (GSEA) was used to explore the biological function and signal pathway of the Hub genes and corresponding co-expressed genes.ResultsWe identified 346 differentially expressed genes (DEGs), including 117 upregulated and 229 downregulated. Four Hub genes (AURKA, CCNB1, EXO1 and CCNA2) were selected by survival analysis and differential expression validation. The protein and mRNA expression levels of AURKA, CCNB1, EXO1 and CCNA2 were higher in CRC tissues than in adjacent tissues. There were varying degrees of immune cell infiltration and gene mutation of Hub genes, especially B cells and CD8+ T cells. The results of GSEA showed that Hub genes and their co-expressed genes mainly participated in chromosome segregation, DNA replication, translational elongation and cell cycle.ConclusionOverexpression of AURKA, CCNB1, CCNA2 and EXO1 had a better prognosis for CRC and this effect was correlation with gene mutation and infiltration of immune cells.
| S-EPMC11191499 | biostudies-literature
Project description:Colorectal cancer (CRC) is one of the most prevalent types of malignant tumors. It's vital to explore new biomarkers and potential therapeutic targets in CRC lung metastasis through adopting integrated bioinformatics tools. Multiple cohort datasets and databases were integrated to clarify and verify potential key candidate biomarkers and signal transduction pathways in CRC lung metastasis. DAVID, STRING, UALCAN, GEPIA, TIMER, cBioPortal, THE HUMAN PROTEIN ATLAS, GSEA 4.3.2, FUNRICH 3.1.3, and R 4.2.3 were utilized in this study. The enriched biological processes and pathways modulated by the differentially expressed genes (DEGs) were determined with Gene Ontology, Kyoto Encyclopedia of Genes and Genomes. The search tool Retrieval of Interacting Genes and Cytoscape were used to construct a protein-protein interaction network among DEGs. Four hundred fifty-nine colorectal primary cancer and lung metastatic gene expression profiles were screened from 3 gene expression profiles (GSE41258, GSE68468, and GSE41568). Forty-one upregulated genes and 8 downregulated genes were identified from these 3 gene expression profiles and verified by the transcriptional levels of hub genes in other GEO datasets and The Cancer Genome Atlas database. Two pathways (immune responses and chemokine receptors bind chemokines), 13 key DEGs, 6 hub genes (MMP3, SFTPD, ABCA3, CLU, APOE, and SPP1), and 2 biomarkers (APOE, SPP1) with significantly prognostic values were screened. Forty-nine DEGs were identified as potential candidate diagnostic biomarkers for patients with CRC lung metastasis in present study. Enrichment analysis indicated that immune responses and chemokine receptors bind chemokines may play a leading role in lung metastasis of CRC, and further studies are needed to validate these findings.
| S-EPMC10939685 | biostudies-literature
Project description:Colorectal cancer (CRC) is the third most diagnosed malignancy and the second leading cause of cancer death. The objective was to identify novel hub genes that were helpful for prognosis and targeted therapy in CRC. GSE23878, GSE24514, GSE41657, GSE81582 were filtered from the gene expression omnibus (GEO). Differentially expressed genes (DEGs) were identified through GEO2R, which were enriched in the GO term and KEGG pathway in DAVID. PPI network was constructed and analyzed using STRING and hub genes were screened out. The relationships between hub genes and prognoses in CRC were evaluated in GEPIA based on the cancer genome atlas (TCGA) and genotype-tissue expression (GTEx). The transcription factors and miRNA-mRNA interaction networks for hub genes were performed using miRnet and miRTarBase. The relationship between hub genes and tumor-infiltrating lymphocytes were analyzed in TIMER. The protein levels of hub genes were identified in HPA. The expression levels of hub gene in CRC and its effect on the biological effect of CRC cells were identified in vitro. As hub genes, the mRNA levels of BIRC5, CCNB1, KIF20A, NCAPG, and TPX2 were highly expressed in CRC and had excellent prognostic value. The BIRC5, CCNB1, KIF20A, NCAPG, and TPX2 were closely associated with transcription factors, miRNAs, tumor-infiltrating lymphocytes, suggesting their involvement in the regulation of CRC. BIRC5 highly expressed in CRC tissues and cells, and promoted the proliferation, migration, and invasion of CRC cells. BIRC5, CCNB1, KIF20A, NCAPG, and TPX2 are hub genes that serve as promising prognostic biomarkers in CRC. BIRC5 plays an important role in the development and progression of CRC.
| S-EPMC10300223 | biostudies-literature
Project description:BACKGROUND Colorectal cancer (CRC) is a common malignant tumor with high incidence and mortality worldwide. The aim of this study was to evaluate the association between differentially expressed genes (DEGs), which may function as biomarkers for CRC prognosis and therapies, and the clinical outcome in patients with CRC. MATERIAL AND METHODS A total of 116 normal mucous tissue and 930 CRC tissue datasets were downloaded from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA). After screening DEGs based on limma package in R. Gene Ontology (GO) and KEGG enrichment analysis as well as the protein-protein interaction (PPI) networks were performed to predict the function of these DEGs. Meanwhile, Cox proportional hazards regression was used to build a prognostic model of these DEGs. Then, Kaplan-Meier risk analysis was used to test the model in TCGA datasets and validation datasets. RESULTS In the present study, 300 DEGs with 100 upregulated genes and 200 downregulated genes were identified. The PPI networks including 162 DEGs and 256 nodes were constructed and 2 modules with high degree were selected. Moreover, 5 genes (MMP1, ACSL6, SMPD1, PPARGC1A, and HEPACAM2) were identified using the Cox proportional hazards stepwise regression. Kaplan-Meier risk curve in the TCGA and validation cohorts showed that high-risk group had significantly poor overall survival than the low-risk group. CONCLUSIONS Our study provided insights into the mechanisms of CRC formation and found 5 prognostic genes, which could potentially inform further studies and clinical therapies.
| S-EPMC6065283 | biostudies-other