Systematic analyses of a novel circRNA-related miRNAs prognostic signature for Cervical Cancer.
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ABSTRACT:
SUBMITTER: Wang S
PROVIDER: S-EPMC9241030 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Cervical cancer is the second most commonly diagnosed cancer in women. Novel prognostic biomarkers are required to predict the progression of cervical cancer. Cervical cancer expression data were obtained from The Cancer Genome Atlas (TCGA) database. MicroRNAs (miRNAs) significantly differentially expressed between early‑ and advanced‑stage samples were identified by expression analysis. An optimal subset of signature miRNAs for pathologic stage prediction was delineated using the random forest algorithm and was used for the construction of a cervical cancer‑specific support vector machine (SVM) classifier. The roles of signature miRNAs in cervical cancer were analyzed by functional annotation. In total, 44 significantly differentially expressed miRNAs were identified. An optimal subset of 7 signature miRNAs was identified, including hsa‑miR‑144, hsa‑miR‑147b, hsa‑miR‑218‑2, hsa‑miR‑425, hsa‑miR‑451, hsa‑miR‑483 and hsa‑miR‑486. The signature miRNAs were used to construct an SVM classifier and exhibited a good performance in predicting pathologic stages of samples. SVM classification was found to be an independent prognostic factor. Functional enrichment analysis indicated that these signature miRNAs are involved in tumorigenesis. In conclusion, the subset of signature miRNAs could potentially serve as a novel diagnostic and prognostic predictor for cervical cancer.
| S-EPMC6489013 | biostudies-literature
Project description:AimCervical cancer is a common malignant carcinoma of the gynecological tract with high morbidity and mortality. Therefore, it is crucial to elucidate the pathogenesis, prevention, diagnosis and prognosis of cervical cancer by searching for the involved key genes.MethodIn this study, the alternative splicing (AS) events of 253 patients with cervical cancer were analyzed, and 41,766 AS events were detected in 9961 genes. Univariate analysis was performed to screen prognostic AS events. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was used to identify the pathways in which these AS events were involved.ResultsWe found that exon skip (ES) is the main AS event in patients with cervical cancer. There was pronounced consistency between the genes involved in overall survival and those involved in recurrence. At the same time, we found that a gene may exhibit several different types of AS events, and these different AS events may be related to prognosis. Four characteristic genes, HSPA14, SDHAF2, CAMKK2 and TM9SF1, that can be used as prognostic markers for cervical cancer were selected.ConclusionThe importance of AS events in the development of cervical cancer and prediction of prognosis was revealed by a large amount of data at the whole genome level, which may provide a potential target for cervical cancer treatment. We also provide a new method for exploring the pathogenesis of cervical cancer to determine clinical treatment and prognosis more accurately.
| S-EPMC6865056 | biostudies-literature
Project description:Cervical cancer and endometrial cancer remain serious threats to women's health. Even though some patients can be treated with surgery plus chemoradiotherapy as a conventional option, the overall efficacy is deemed unsatisfactory. As such, the development for new treatment approaches is truly necessary. In recent years, immunotherapy has been widely used in clinical practice and it is an area of great interest that researchers are keeping attention on. However, a thorough immune-related genes (IRGs) study for cervical cancer and endometrial cancer is still lacking. We therefore aim to make a comprehensive evaluation of IRGs through bioinformatics and large databases, and also investigate the relationship between the two types of cancer. We reviewed the transcriptome RNAs of IRGs and clinical data based on the TCGA database. Survival-associated IRGs in cervical/endometrial cancer were identified using univariable and multivariable Cox proportional-hazard regression analysis for developing an IRG signature model to evaluate the risk of patients. In the end, this model was validated based on the enrichment analyses through GO, KEGG, and GSEA pathways, Kaplan-Meier survival curve, ROC curves, and immune cell infiltration. Our results showed that out of 25/23 survival-associated IRGs for cervical/endometrial cancer, 13/12 warranted further examination by multivariate Cox proportional-hazard regression analysis and were selected to develop an IRGs signature model. As a result, enrichment analyses for high-risk groups indicated main enriched pathways were associated with tumor development and progression, and statistical differences were found between high-risk and low-risk groups as shown by Kaplan-Meier survival curve. This model could be used as an independent measure for risk assessment and was considered relevant to immune cell infiltration, but it had nothing to do with clinicopathological characteristics. In summary, based on comprehensive analysis, we obtained the IRGs signature model in cervical cancer (LTA, TFRC, TYK2, DLL4, CSK, JUND, NFATC4, SBDS, FLT1, IL17RD, IL3RA, SDC1, PLAU) and endometrial cancer (LTA, PSMC4, KAL1, TNF, SBDS, HDGF, LTB, HTR3E, NR2F1, NR3C1, PGR, CBLC), which can effectively evaluate the prognosis and risk of patients and provide justification in immunology for further researches.
| S-EPMC7385326 | biostudies-literature
Project description:BackgroundCervical cancer (CC) is a major malignancy affecting women worldwide, with limited treatment options for patients with advanced disease. The aim of this study was to identify novel prognostic biomarkers for CC.MethodsRNA-Seq data from four Gene Expression Omnibus datasets (GSE5787, GSE6791, GSE26511, and GSE63514) were used to identify differentially expressed genes (DEGs) between CC and normal cervical tissues. Functional and enrichment analyses of the DEGs were performed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and the Database for Annotation, Visualization, and Integrated Discovery (DAVID). The Oncomine database, Cytoscape software, and Kaplan-Meier survival analyses were used for in-depth screening of hub DEGs. The Cox regression was then used to develop a prognostic signature, which was in turn used to create a nomogram.ResultsA total of 207 DEGs were identified in the tissue samples, eight of which were prognostically significant in terms of overall survival (OS). Thereafter, a novel four-gene signature consisting of DSG2, MMP1, SPP1, and MCM2 was developed and validated using stepwise Cox analysis. The area under the receiver operating characteristic (ROC) curve (AUC) values were 0.785, 0.609, and 0.686 in the training, verification, and combination groups, respectively. The protein expression levels of the four genes were well validated by the western blotting. Moreover, the nomogram analysis showed that a combination of this four-gene signature plus lymph node metastasis (LNM) status effectively predicted the 1- and 3-year OS probabilities of CC patients with accuracies of 69.01% and 83.93%, respectively.ConclusionsWe developed a four-gene signature that can accurately predict the prognosis in terms of OS, of CC patients, and could be a valuable tool for designing treatment strategies.
| S-EPMC7758149 | biostudies-literature
Project description:Pancreatic cancer (PC) is a malignant tumor of the digestive system with a poor prognosis. PC patients with pancreatitis have a worse prognosis. But nobody reported the relationship between inflammation and prognosis in PC. Based on this, we are going to explore inflammation-related prognostic signature to predict patients' survival and potential therapeutic target. We screened gene expression profile and corresponding clinical information of patients from The Cancer Genome Atlas (TCGA) database. Gene set enrichment analysis (GSEA) was performed to identify differentially expressed genes (DEGs) between tumor and normal tissues with P value < .05. Univariate and multivariate Cox regression analysis was applied to identify possible prognostic inflammation genes and establish an inflammation-related risk score system, which was validated by Kaplan-Meier and Receiver operating characteristic (ROC) curves. Finally, we used the TISIDB database to predict targeted drugs for up-regulated gene hepatocyte growth factor receptor (MET) and used AUTODOCK software for molecular docking. We built a prognostic model consisted of 3 inflammation-related genes (tumor necrosis factor receptor associated factor 1/TFAR1, tyrosine kinase 2/TYK2, MET). According to the median value of those genes' risk score, PC patients were ranked into high- (88) and low-risk (89) groups. Then, the results of the Kaplan-Meier curves and the area under the curve (AUC) of the ROC curves showed this model had a good predictive power (P < .001, AUC = 0.806). The result of human protein atlas (HPA) database showed the expression of TRAF1 and TYK2 were low in pancreatic cancer, the expression of MET was high. TISIDB database founded brigatinib could target to MET. And AUTODOCK showed brigatinib had a nice docking with MET. Taken together, our study suggested that inflammation-associated prognostic signature might be used as novel biomarkers for predicting prognosis in PC patients and potential therapeutic target of the disease.
| S-EPMC10869063 | biostudies-literature
Project description:BackgroundFerroptosis is an iron-dependent cell death, which is distinct from the other types of regulated cell death. Considerable studies have demonstrated that ferroptosis is involved in the biological process of various cancers. However, the role of ferroptosis in cervical cancer (CC) remains unclear. This study aims to explore the ferroptosis-related prognostic genes (FRPGs) expression profiles and their prognostic values in CC.MethodsThe ferroptosis-related genes (FRGs) were obtained from The Cancer Genome Atlas (TCGA) and FerrDb databases. Core FRGs were determined by the Search Tool for the Retrieval of Interacting Genes (STRING) website. FRPGs were identified using univariate and multivariate Cox regressions, and the ferroptosis-related prognostic model was constructed. FRPGs were verified in clinical specimens. The relationship between FRPGs and tumor infiltrating immune cells were assessed through the CIBERSORT algorithm and the LM22 signature matrix. Bioinformatics functions of FRPGs were explored with the Database for Annotation, Visualization, and Integrated Discovery (DAVID).ResultsThirty-three significantly up-regulated and 28 down-regulated FRGs were screened from databases [P<0.05; false discovery rate (FDR) <0.05; and |log2 fold change (FC)| ≥2]. Twenty-four genes were found closely interacting with each other and regarded as hub genes (degree ≥3). Solute carrier family 2 member 1 (SLC2A1), carbonic anhydrases IX (CA9), and dual oxidase 1 (DUOX1) were identified as independent prognostic signatures for overall survival (OS) in a Cox regression. Time-dependent receiver operating characteristic (ROC) curves showed the predictive ability of the ferroptosis-related prognostic model, especially for 1-year OS [area under the curve (AUC) =0.76]. Consistent with the public data, our experiments demonstrated that the mRNA levels of SLC2A1 and DUOX1, and the protein levels of SLC2A1, DUOX1, and CA9 were significantly higher in the tumor tissues. Further analysis showed that there was a significant difference in the proportion of tumor infiltrating immune cells between the low- and high-risk group based on our prognostic model. The function enrichment of FRPGs was explored by applying Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses.ConclusionsIn this study, the features of FRPGs in CC were pictured. The results implicated that targeting ferroptosis may be a new reliable biomarker and an alternative therapy for CC.
| S-EPMC11319947 | biostudies-literature
Project description:Ferroptosis, a newly discovered iron-dependent form of cell death, contributes to various pathologies; however, the prognostic value of ferroptosis-related genes (FRGs) in cervical cancer (CC) remains unclear. Herein, we identified 15 differentially expressed FRGs based on data from The Cancer Genome Atlas database. Ten FRGs that correlated with prognosis were screened by univariate Cox regression analysis. The least absolute shrinkage and selection operator regression model was performed to develop a novel prognostic signature. A four-gene model was built to separate samples into high-risk and low-risk groups. Overall survival was lower in the high-risk group than in the low-risk group (p < 0.05). Receiver operating characteristic curve showed a good diagnostic efficiency of the signature. The risk score was identified as an independent prognostic factor via multivariate Cox regression. A functional analysis further revealed a difference in the immune status between the two risk groups. To conclude, we constructed a novel prognostic signature based on FRGs. Targeting ferroptosis may represent a promising approach for the treatment of CC.
| S-EPMC8806749 | biostudies-literature
Project description:Colorectal cancer (CRC) is one of the most prevalent and deadliest illnesses all around the world. Growing proofs demonstrate that tumor-associated macrophages (TAMs) are of critical importance in CRC pathogenesis, but their mechanisms remain yet unknown. The current research was designed to recognize underlying biomarkers associated with TAMs in CRC. We screened macrophage-related gene modules through WGCNA, selected hub genes utilizing the LASSO algorithm and COX regression, and established a model. External validation was performed by expression analysis using datasets GSE14333, GSE74602, and GSE87211. After validating the bioinformatics results using real-time quantitative reverse transcription PCR, we identified SPP1, C5AR1, MMP3, TIMP1, ADAM8 as potential biomarkers associated with macrophages in CRC.
| S-EPMC10837438 | biostudies-literature
Project description:Accumulating evidence implies that long noncoding RNAs (lncRNAs) play a crucial role in predicting survival for Hepatocellular carcinoma (HCC) patients. This study aims to capture the current research hotspots of HCC, based on the analysis of publications related to HCC research from 2013 to 2017, and to identify a novel lncRNA signature for HCC prognosis through the data mining in The Cancer Genome Atlas (TCGA). "Prognosis" and "biomarker" were located in the core of the HCC research hotspot. Moreover, long noncoding RNA was the top one research frontier in HCC research. The associations between survival outcome and the expression of lncRNAs were evaluated by the univariate and multivariate Cox proportional hazards regression analyses. Four lncRNAs (LINC00261, TRELM3P, GBP1P1, and CDKN2B-AS1) were identified as significantly correlated with overall survival (OS). These four lncRNAs were gathered as a single prognostic signature. There was a significant positive correlation between HCC patients with low-risk scores and overall survival (HR = 1.802, 95%CI [1.224-2.652], P = .003). Further analysis suggested that the prognostic value of this four-lncRNA signature was independent in clinical features. The enrichment analysis of prognostic lncRNA-related gene was performed to find out the related pathways. Our study indicates that this novel lncRNA expression signature may be a useful biomarker of the prognosis for HCC patients, based on bioinformatics analysis.
| S-EPMC6051236 | biostudies-literature
Project description:BackgroundmiRNAs and genes can serve as biomarkers for the prognosis and therapy of cervical tumors whose metastasis into lymph nodes is closely associated with disease progression and poor prognosis.MethodsR software and Bioconductor packages were employed to identify differentially expressed miRNAs (DEMs) from The Cancer Genome Atlas (TCGA) database. GEO2R detected differentially expressed genes (DEGs) in the GSE7410 dataset originating from the Gene Expression Omnibus (GEO). A Cox proportional hazard regression model was established to select prognostic miRNA biomarkers. Online tools such as TargetScan and miRDB predicted target genes, and overlapping DEGs and target genes were defined as consensus genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and Gene Ontology (GO) function annotations were performed to discern the potential functions of consensus genes. STRING and Cytoscape screened key genes and constructed a regulatory network.ResultsA combination of four miRNAs (down-regulated miR-502 and miR-145, up-regulated miR-142 and miR-33b) was identified as an independent prognostic signature of cervical cancer. A total of 94 consensus genes were significantly enriched in 7 KEGG pathways and 19 GO function annotations including the cAMP signaling pathway, the plasma membrane, integral components of the plasma membrane, cell adhesion, etc. The module analysis suggested that CXCL12, IGF1, PTPRC CDH5, RAD51B, REV3L, and WDHD1 are key genes that significantly correlate with cervical cancer lymph node metastasis.ConclusionsThis study demonstrates that a four-miRNA signature can be a prognostic biomarker, and seven key genes are significantly associated with lymph node metastasis in cervical cancer patients. These miRNAs and key genes have the potential to be therapeutic targets for cervical cancer. Among them, two miRNAs (miR-502 and miR-33b) and two key genes (PTPRC and CDH5) were first reported to be potential novel biomarkers for cervical cancer. The current study further characterizes the progression of lymph node metastasis and mechanism of cervical tumors; therefore, it provides a novel diagnostic indicator and therapeutic targets for future clinical treatments.
| S-EPMC7226536 | biostudies-literature