Dataset Information

Identification of a prognostic 4-mRNA signature in laryngeal squamous cell carcinoma.

ABSTRACT: Background: Laryngeal squamous cell carcinoma (LSCC) is one of the most common malignancy in the respiratory tract and could reduce the quality of life seriously like dyspnea, dysphonia and dysphagia. Moreover, 5-year survival rate has decreased over the past 40 years. This study was designed to identify mRNAs that related to prognosis in LSCC to enable early detection and outcome improvement. Methods: Gene expression profiles from Gene Expression Omnibus (GEO) (GSE59102, GSE84957) and The Cancer Genome Atlas (TCGA) were analyzed to identify differentially expressed genes (DEGs) with the help of bioinformatics tools. Functional enrichment analyses including Gene Ontology (GO) and pathway analysis were carried out to investigate the role of those genes and underlying molecular mechanisms in LSCC. Cox's regression analyses (univariate, LASSO and multivariate in order) were utilized to identify DEGs related with patients' overall survival and a 4-mRNA-based prognostic risk score model was established. Univariate and multivariate Cox's regression analyses were then performed on LSCC data (90 patients left) to identify independent predictors of OS, including the signature and clinicopathologic variables. The prognostic value of the gene signature was further validated and the genes were analyzed by GEPIA to get pan-cancer expression profiles. Results: 444 differentially expressed mRNAs (250 up-regulated, 194 down-regulated) were identified based on the threshold of fold change > 2 and adjusted p value < 0.05. Univariate Cox's regression analysis showed that high risk score (HR: 3.056, 95% confidence interval [CI]: 0.135-0.649, p<0.001) and female (HR: 0.296, 95% CI: 2.020-4.624, p=0.002) were associated with relatively poor prognosis. Further multivariate Cox's regression analysis indicated that risk score and gender were independent prognostic factors (p<0.05). The risk score model could stratify patients into high- and low‑risk groups, which presents significantly differential overall survival (p= 8.252e-04). The AUCs of 1-, 3- and 5-year OS were 0.724, 0.783 and 0.818, respectively. Conclusions: Our study provides evidence that the four-mRNA signature could serve as a biomarker to predict prognosis in LSCC, especially in long-term.

SUBMITTER: Zhang C

PROVIDER: S-EPMC8408111 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundOral squamous cell carcinoma (OSCC) is a commonly encountered head and neck malignancy. Increasing evidence shows that there are abnormal immune response and chronic cell hypoxia in the development of OSCC. However, there is a lack of a reliable hypoxia-immune-based gene signature that may serve to accurately prognosticate OSCC.MethodsThe mRNA expression data of OSCC patients were extracted from the TCGA and GEO databases. Hypoxia status was identified using the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm. Both ESTIMATE and single-sample gene-set enrichment analysis (ssGSEA) were used for further evaluation of immune status. The DEGs in different hypoxia and immune status were determined, and univariate Cox regression was used to identify significantly prognostic genes. A machine learning method, least absolute shrinkage and selection operator (LASSO) Cox regression analysis, allowed us to construct prognostic gene signature to predict the overall survival (OS) of OSCC patients.ResultsA total of 773 DEGs were identified between hypoxia high and low groups. According to immune cell infiltration, patients were divided into immune high, medium, and low groups and immune-associated DEGs were identified. A total of 193 overlapped DEGs in both immune and hypoxia status were identified. With the univariate and LASSO Cox regression model, eight signature mRNAs (FAM122C, RNF157, RANBP17, SOWAHA, KIAA1211, RIPPLY2, INSL3, and DNAH1) were selected for further calculation of their respective risk scores. The risk score showed a significant association with age and perineural and lymphovascular invasion. In the GEO validation cohort, a better OS was observed in patients from the low-risk group in comparison with those in the high-risk group. High-risk patients also demonstrated different immune infiltration characteristics from the low-risk group and the low-risk group showed potentially better immunotherapy efficacy in contrast to high-risk ones.ConclusionThe hypoxia-immune-based gene signature has prognostic potential in OSCC.

Project description:Background:Recurrence remains a major obstacle to long-term survival of laryngeal squamous cell carcinoma (LSCC). We conducted a genome-wide integrated analysis of methylation and the transcriptome to establish methylation-driven genes prognostic signature (MDGPS) to precisely predict recurrence probability and optimize therapeutic strategies for LSCC. Methods:LSCC DNA methylation datasets and RNA sequencing (RNA-seq) dataset were acquired from the Cancer Genome Atlas (TCGA). MethylMix was applied to detect DNA methylation-driven genes (MDGs). By univariate and multivariate Cox regression analyses, five genes of DNA MDGs was developed a recurrence-free survival (RFS)-related MDGPS. The predictive accuracy and clinical value of the MDGPS were evaluated by receiver operating characteristic (ROC) and decision curve analysis (DCA), and compared with TNM stage system. Additionally, prognostic value of MDGPS was validated by external Gene Expression Omnibus (GEO) database. According to 5 MDGs, the candidate small molecules for LSCC were screen out by the CMap database. To strengthen the bioinformatics analysis results, 30 pairs of clinical samples were evaluated by digoxigenin-labeled chromogenic in situ hybridization (CISH). Results:A total of 88 DNA MDGs were identified, and five RFS-related MDGs (LINC01354, CCDC8, PHYHD1, MAGEB2 and ZNF732) were chosen to construct a MDGPS. The MDGPS can effectively divide patients into high-risk and low-risk group, with the area under curve (AUC) of 0.738 (5-year RFS) and AUC of 0.74 (3-year RFS). Stratification analysis affirmed that the MDGPS was still a significant statistical prognostic model in subsets of patients with different clinical variables. Multivariate Cox regression analysis indicated the efficacy of MDGPS appears independent of other clinicopathological characteristics. In terms of predictive capacity and clinical usefulness, the MDGPS was superior to traditional TNM stage. Additionally, the MDGPS was confirmed in external LSCC cohorts from GEO. CMap matched the 9 most significant small molecules as promising therapeutic drugs to reverse the LSCC gene expression. Finally, CISH analysis in 30 LSCC tissues and paired adjacent normal tissues revealed that MAGEB2 has significantly higher expression of LSCC compared to adjacent non-neoplastic tissues; LINC01354, CCDC8, PHYHD1, and ZNF732 have significantly lower expression of LSCC compared to adjacent non-neoplastic tissues, which were in line with bioinformatics analysis results. Conclusion:A MDGPS, with five DNA MDGs, was identified and validated in LSCC patients by combining transcriptome and methylation datasets analysis. Compared TNM stage alone, it generates more accurate estimations of the recurrence prediction and maybe offer novel research directions and prospects for individualized treatment of patients with LSCC.

Project description:BackgroundOral squamous cell carcinoma (OSCC) is the most common type of head and neck squamous cell carcinoma with an unsatisfactory prognosis. The aim of this study was to identify potential prognostic mRNA biomarkers of OSCC based on analysis of The Cancer Genome Atlas (TCGA).MethodsExpression profiles and clinical data of OSCC patients were collected from TCGA database. Univariate Cox analysis and the least absolute shrinkage and selection operator Cox (LASSO Cox) regression were used to primarily screen prognostic biomarkers. Then multivariate Cox analysis was performed to build a prognostic model based on the selected prognostic mRNAs. Nomograms were generated to predict the individual's overall survival at 3 and 5 years. The model performance was assessed by the time-dependent receiver operating characteristic (ROC) curve and calibration plot in both training cohort and validation cohort (GSE41613 from NCBI GEO databases). In addition, machine learning was used to assess the importance of risk factors of OSCC. Finally, in order to explore the potential mechanisms of OSCC, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was completed.ResultsThree mRNAs (CLEC3B, C6 and CLCN1) were finally identified as a prognostic biomarker pattern. The risk score was imputed as: (-0.38602 × expression level of CLEC3B) + (-0.20632 × expression level of CLCN1) + (0.31541 × expression level of C6). In the TCGA training cohort, the area under the curve (AUC) was 0.705 and 0.711 for 3- and 5-year survival, respectively. In the validation cohort, AUC was 0.718 and 0.717 for 3- and 5-year survival. A satisfactory agreement between predictive values and observation values was demonstrated by the calibration curve in the probabilities of 3- and 5- year survival in both cohorts. Furthermore, machine learning identified the 3-mRNA signature as the most important risk factor to survival of OSCC. Neuroactive ligand-receptor interaction was most enriched mostly in KEGG pathway analysis.ConclusionA 3-mRNA signature (CLEC3B, C6 and CLCN1) successfully predicted the survival of OSCC patients in both training and test cohort. In addition, this signature was an independent and the most important risk factor of OSCC.

Project description:Studies have shown that long non-coding RNA (lncRNA) may act as the carcinogenic factor or tumor suppressor of laryngeal squamous cell carcinoma (LSCC). This study aims to identify the prognostic value and potential target protein-coding genes (PCGs) of lncRNAs in LSCC. The LSCC datasets were collected from The Cancer Genome Atlas (TCGA). Statistical and bioinformatic methods were used to establish and evaluate the prognostic model, identify the correlation between lncRNAs and clinical characteristics, and screen for PCGs co-expressed with lncRNAs. Weighted gene co-expression network analysis (WGCNA) identified PCG modules associated with clinical characteristics. The expression of lncRNAs and PCGs was analyzed using our LSCC patients by RT-qPCR. LINC02154, LINC00528, SPRY4-AS1, TTTY14, LNCSRLR, and KLHL7-DT were selected to establish the prognostic model. The overall survival (OS) of low-risk patients forecasted by the model was significantly better than high-risk patients. Receiver operating characteristic (ROC) curve and concordance index (C-index) validated the accuracy of the prognostic model. Chi-square test showed that six lncRNAs were associated with one of the clinical characteristics, i.e., gender, clinical stage, T and N stage, respectively. WGCNA identified PCG modules associated with gender, clinical stage, T and N stage. We took the intersection of the PCG modules of WGCNA, the differentially expressed PCGs between LSCC and normal samples, and the PCGs co-expressed with six lncRNAs. The intersection PCGs survival analysis showed that four PCGs, i.e., STC2, TSPAN9, SMS, and TCEA3 affected the OS of LSCC. More importantly, the differential expression of six lncRNAs and four PCGs between LSCC and normal samples was verified by our LSCC patients. In conclusion, we successfully established a prognostic model based on six-lncRNA RiskScore and initially screened the potential target PCGs of six lncRNAs for further basic and clinical research.

Project description:BackgroundThe growing understanding of cancer biology and the establishment of new treatment modalities has not yielded the expected results in terms of survival for Laryngeal Squamous Cell Cancer (LSCC). Early diagnosis, as well as prompt identification of patients with high risk of relapse would ensure greater chance of therapeutic success. However, this goal remains a challenge due to the absence of specific biomarkers for this neoplasm.MethodsSerum samples from 45 LSCC patients and 23 healthy donors were collected for miRNA expression profiling by TaqMan Array analysis. Additional 20 patients and 42 healthy volunteers were included for the validation set, reaching an equal number of clinical samples for each group. The potential diagnostic ability of the such identified three-miRNA signature was confirmed by ROC analysis. Moreover, each miRNA was analyzed for the possible correlation with HNSCC patients' survival and TNM status by online databases Kaplan-Meier (KM) plotter and OncomiR. In silico analysis of common candidate targets and their network relevance to predict shared biological functions was finally performed by PANTHER and GeneMANIA software.ResultsWe characterized serum miRNA profile of LSCC patients identifying a novel molecular signature, including miR-223, miR-93 and miR-532, as circulating marker endowed with high selectivity and specificity. The oncogenic effect and the prognostic significance of each miRNA was investigated by bioinformatic analysis, denoting significant correlation with OS. To analyse the molecular basis underlying the pro-tumorigenic role of the signature, we focused on the simultaneously regulated gene targets-IL6ST, GTDC1, MAP1B, CPEB3, PRKACB, NFIB, PURB, ATP2B1, ZNF148, PSD3, TBC1D15, PURA, KLF12-found by prediction tools and deepened for their functional role by pathway enrichment analysis. The results showed the involvement of 7 different biological processes, among which inflammation, proliferation, migration, apoptosis and angiogenesis.ConclusionsIn conclusion, we have identified a possible miRNA signature for early LSCC diagnosis and we assumed that miR-93, miR-223 and miR-532 could orchestrate the regulation of multiple cancer-related processes. These findings encourage the possibility to deepen the molecular mechanisms underlying their oncogenic role, for the desirable development of novel therapeutic opportunities based on the use of short single-stranded oligonucleotides acting as non-coding RNA antagonists in cancer.

Dataset Information

Identification of a prognostic 4-mRNA signature in laryngeal squamous cell carcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets