A Panel of Five-lncRNA Signature as a Potential Biomarker for Predicting Survival in Gastric and Thoracic Cancers.
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ABSTRACT:
SUBMITTER: Shao J
PROVIDER: S-EPMC8076896 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Long non-coding RNAs (lncRNAs) have been considered as biomarkers for the carcinogenesis and development of various cancers. However, the prognostic significance of lncRNAs in renal cell carcinoma (RCC) remains unclear. This study aimed to determine the predictive ability of lncRNAs in clear cell RCC (ccRCC). Among the cohort of kidney renal clear cell carcinoma (KIRC) of the The Cancer Genome Atlas (TCGA), 525 patients were enrolled in our study. Expression of lncRNAs based on RNAseq was obtained from TCGA. Kaplan-Meier prognostic analysis and a Cox proportional hazards regression model were used to assess related factors. The lncRNA signature was then validated in an independent cohort of an additional 60 ccRCC patients. Hierarchical clustering of the KIRC TCGA dataset identified 26 differentially expressed lncRNAs (11 down-regulated and 15 up-regulated) using average linkage clustering. Kaplan-Meier survival analysis identified 30 statistically significant lncRNAs that strongly predicted prognosis, with 4 ccRCC-specific lncRNAs (TCL6, PVT1, MIR155HG, and HAR1B) being differentially expressed and correlating significantly with OS. Patients assigned to the high-risk group were associated with poor OS compared with patients in the low-risk group (HR = 2.57; 95%CI, 1.89-3.50; p < 0.001). This finding was validated in the Tongji Hospital cohort, and the four-lncRNA signature was shown to be significantly predictive of ccRCC prognosis (p < 0.001). In this study, we constructed an applicable four-lncRNA-based classifier as a reliable prognostic and predictive tool for OS in patients with ccRCC.
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Project description:Cholangiocarcinoma (CCA) is a fatal disease with dismal survival rates. Long non-coding RNA (lncRNA) expression profiling as potential prognostic biomarkers play critical roles in tumor initiation, development, and poor prognosis. Identifying specific lncRNA to predict the prognosis of CCA patients in the early stages is very important for improving a patient's survival. In the current study, we aimed to establish a novel risk-stratification lncRNA signature panel in CCA. The initial lncRNA discovery was identified in The Cancer Genome Atlas database (TCGA cohort). The Cox regression analysis was used to establish the lncRNA prognostic model and the receiver operating characteristic (ROC) curve analysis was performed to assess the specificity and sensitivity of the model. This was followed by independent validation of the lncRNA signature in the CCA patients from the First Affiliated Hospital of Wenzhou Medical University (WMU cohort). Furthermore, by using the Gene Ontology function and Kyoto Encyclopedia Gene and Genome pathway enrichment analysis, we explored the potential function of prognosis lncRNA. Finally, five lncRNA (HULC; AL359715.5; AC006504.8; AC090114.2; AP00943.4) were screened to establish the predictive model that significantly associated with poor overall survival(HR:4.879;95%CI,1.587-14.996;p=0.006). This five-lncRNA signature model showed excellent accuracy in the TCGA cohort (AUC=0.938), and also robustly predicted survival in the validation WMU cohort(AUC=0.816). Functional enrichment analysis suggested prognostic lncRNA was primarily associated with CCA-related biological processes. Our data established a novel lncRNA signature model for CCA risk-stratification and robust identification of CCA patients with poor molecular genotypes. Moreover, it revealed new molecular mechanisms of CCA.
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Project description:There is increasing evidence to suggest that miRNAs play an important role in predicting cancer survival. To identify a panel of miRNA signature that can divided tumor from normal bladder using miRNA expression levels, and to assess the prognostic value of this specific miRNA markers in bladder cancer (BCa).A comprehensive meta-review of published miRNA expression profiles that compared BCa and adjacent normal tissues was performed to determine candidate miRNAs as prognostic biomarkers for BCa. Vote-counting strategy and Robust Rank Aggregation method were used to identify significant meta-signature miRNAs.We identified an eight-miRNA signature including three upregulated (miR-141, miR-200c, miR-21) and five downregulated (miR-145, miR-125, miR-199a, let-7c and miR-99a) miRNAs for the prediction of overall survival (OS) using TCGA dataset, and validated in our 48 BCa patients. X-tile plot was used to generate the optimum cut-off point and Kaplan-Meier method was used to calculate OS. A linear prognostic model of eight miRNAs was constructed and weighted by the importance scores from the supervised principal component method to divide patients into high- and low-risk groups. Patients assigned to the high-risk group were associated with poor OS compared with patients in the low-risk group (HR?=?5.21, p?<?0.001). Our validation cohort of 48 patients confirmed the panel of 8-miRNAs as a reliable prognostic tool for OS in patients with BCa (HR?=?5.04, p?<?0.001).The present meta-analysis identified eight highly significant and consistently dysregulated miRNAs from 19 datasets. We also constructed an eight-miRNA signature which provided predictive and prognostic value that complements traditional clinicopathological risk factors.
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Project description:Some reports have evaluated the prognostic relevance of microRNAs (miRNAs) in patients with pancreatic cancer (PC). However, most studies focused on limited miRNAs with small number of patients. The aim of the study is to identify a panel of miRNA signature that could predict prognosis in PC with the data from The Cancer Genome Atlas (TCGA). A total of 167 PC patients with the corresponding clinical data were enrolled in our study. The miRNAs significantly associated with overall survival (OS) in PC patients were identified with Cox proportional regression model. A risk score formula was developed to evaluate the prognostic value of the miRNA signature in PC. Thirteen miRNAs were identified to be significantly related with OS in PC patients. Patients with high risk score suffered poor overall survival compared with patients who had low risk score. The multivariate Cox regression analyses showed that the miRNA signature could act as an independent prognostic indicator. In addition, the signature might serve as a predicator for treatment outcome. Our study identified a miRNA signature including 13 miRNAs which could serve as an independent marker in prognosis of PC.
| S-EPMC5342502 | biostudies-literature
Project description:Background: Recent studies have highlighted the important roles of long non-coding RNAs (lncRNAs) in pancreatic adenocarcinoma (PCa) prognosis. However, most studies explored a limited number of lncRNAs based on small sample size. Methods: Systematic and comprehensive analysis of the data from The Cancer Genome Atlas (TCGA) was performed to identify a panel of lncRNA signature for predicting prognosis in PCa. Results: A total of 160 PCa patients with complete clinical data were included in our study. Twelve lncRNAs were identified to be significantly associated with overall survival (OS) in PCa patients using Cox regression analysis. A risk score formula was constructed to assess the prognostic value of the lncRNA signature in PCa. Patients with high risk score had worse OS than those with low risk score. The multivariate Cox regression analyses revealed that the lncRNA signature was an independent prognostic factor. Additionally, the signature might act as an indicator to predict treatment outcome. Functional enrichment analyses showed that the lncRNAs might involve in several molecular pathways closely related with PCa such as DNA replication, pancreatic cancer and regulation of tor signaling. Conclusions: Our study demonstrated a lncRNA signature including 12 lncRNAs with the potential to be served as an independent prognostic biomarker of PCa.
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Project description:BackgroundGastric adenocarcinoma (GAD) is one of the most common tumors in the world and the prognosis is still very poor.ObjectiveWe sought to identify reliable prognostic biomarkers for the progression of GAD and the sensitivity to drug therapy.MethodThe RNA sequencing data of GAD was downloaded from the Cancer Genome Atlas (TCGA) database and used for analysis. Differentially expressed, immune-related lncRNA (DEIRlncRNA) was characterized by differential analysis and correlation analysis. Univariate Cox regression analysis was used to identify DEIRlncRNA associated with prognosis. Least absolute shrinkage and selection operator (LASSO) regression analysis allowed us to determine a signature composed of eight IRlncRNAs. Based on this signature, we further performed gene set enrichment analysis (GSEA) and somatic mutation analysis to evaluate the ability of this signature to predict prognosis.ResultsIn total, 72 immune-related lncRNAs (DEIRlncRNAs) with prognostic value were identified. These lncRNAs were used to construct a model containing eight immune-related lncRNAs (8-IRlncRNAs). Based on this risk model, we divided GAD patients into high-risk and low-risk groups. The analysis showed that the prognosis of the two groups was different and that the high-risk group had worse overall survival (OS). Immune cell infiltration analysis showed that the proportion of memory B cells increased in the high-risk group while the proportion of macrophages M1, T cells, CD4 memory-activated cells, and T cell follicular helpers decreased. GSEA results showed that 8-IRlncRNA was significantly enriched in tumorigenesis pathways such as myc. The results of somatic mutation analysis showed that the CDH1 gene was significantly mutated in the high-risk group.ConclusionA prognostic signature of 8-IRlncRNAs in GAD was established and this signature was able to predict the prognosis of GAD patients.
| S-EPMC9061059 | biostudies-literature
Project description:ObjectiveAs a critical feature of cancers, stemness is acknowledged as a contributor to the development of drug resistance in gastric cancer (GC). LncRNAs have been revealed to participate in this process. In this study, we tried to develop a stemness-related lncRNA pair signature as guidance for clinical decisions.MethodsThe analysis was initiated by collecting stemness-related lncRNAs in TCGA cohort. The differentially expressed stemness-related lncRNAs between normal and tumor tissues in GC patients from TCGA datasets were further collected to establish the signature based on Lasso and Cox regression analyses. The predictive efficacy of the signature for chemotherapy and immunotherapy was also tested. The practicality of this signature was also validated by Zhongshan cohort.ResultsA 13-DEsrlncRNA pair-based signature was established. The cutoff point acquired by the AIC algorithm divided the TCGA cohort into high and low risk groups. We found that the low-risk group presented with better survival (Kaplan-Meier analysis, p < 0.001). Cox regression analyse was also conducted to confirm the signature as an independent risk factor for GC {p < 0.001, HR = 1.300, 95% CI (1.231-1.373)]}. As for the practicality of this signature, the IC50 of cytotoxic chemotherapeutics was significantly higher in the high-risk group. The low-risk group also presented with higher immunophenoscore (IPS) in both the "CTLA4+ PD1+" (Mann-Whitney U test, p = 0.019) and "CTLA4- PD1+" (Mann-Whitney U test, p = 0.013) groups, indicating higher sensitivity to immunotherapy. The efficacy of the signature was also validated by Zhongshan cohort.ConclusionsThis study could not only provide a stemness-related lncRNA signature for survival prediction in GC patients but also established a model with predictive potentials for GC patients' sensitivity to chemotherapy and immunotherapy.
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Project description:AbstractEsophageal adenocarcinoma (EAC) is common and aggressive with increasing trend of incidence. Urgent need for an effective signature to assess EAC prognosis and facilitate tailored treatment is required.Differentially expressed mRNAs (DEMs) were identified by analyzing EAC tissues and adjacent normal samples from The Cancer Genome Atlas (TCGA). Then univariate regression analyses were performed to confirm prognostic DEMs. We used least absolute shrinkage and selection operator (LASSO) to build a prognostic mRNA signature whose performance was assessed by Kaplan-Meier curve, receiver operating characteristic (ROC). GSE72874 were used as an external test set. The performances of the signature were also validated in internal TCGA and external test sets. Gene set enrichment analysis (GSEA) and tumor immunity analysis were performed to decipher the biological mechanisms of the signature.A 5-mRNA signature consisted of SLC26A9, SINHCAF, MICB, KRT19, and MT1X was developed to predict prognosis of EAC. The 5-mRNA signature was promising as a biomarker for predicting 3-year survival rate of EAC in the internal test set, the entire TCGA set, and the external test set with areas under the curve (AUC) = 0.849, 0.924, and 0.747, respectively. Patients were divided into low- and high-risk groups based on risk scores of the signature. The high-risk group was mainly associated with cancer-related pathways and low levels of B cell infiltration.The 5-mRNA prognostic signature we identified can reliably predict prognosis and facilitate individualized treatment decisions for EAC patients.
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Project description:BACKGROUND: Lung adenocarcinoma is a heterogernous disease that creates challenges for classification and management. The purpose of this study is to identify specific miRNA markers closely associated with the survival of LUAD patients from a large dataset of significantly altered miRNAs, and to assess the prognostic value of this miRNA expression profile for OS in patients with LUAD. METHODS: We obtained miRNA expression profiles and corresponding clinical information for 372 LUAD patients from The Cancer Genome Atlas (TCGA), and identified the most significantly altered miRNAs between tumor and normal samples. Using survival analysis and supervised principal components method, we identified an eight-miRNA signature for the prediction of overall survival (OS) of LUAD patients. The relationship between OS and the identified miRNA signature was self-validated in the TCGA cohort (randomly classified into two subgroups: n?=?186 for the training set and n?=?186 for the testing set). Survival receiver operating characteristic (ROC) analysis was used to assess the performance of survival prediction. The biological relevance of putative miRNA targets was also analyzed using bioinformatics. RESULTS: Sixteen of the 111 most significantly altered miRNAs were associated with OS across different clinical subclasses of the TCGA-derived LUAD cohort. A linear prognostic model of eight miRNAs (miR-31, miR-196b, miR-766, miR-519a-1, miR-375, miR-187, miR-331 and miR-101-1) was constructed and weighted by the importance scores from the supervised principal component method to divide patients into high- and low-risk groups. Patients assigned to the high-risk group exhibited poor OS compared with patients in the low-risk group (hazard ratio [HR]?=?1.99, P <0.001). The eight-miRNA signature is an independent prognostic marker of OS of LUAD patients and demonstrates good performance for predicting 5-year OS (Area Under the respective ROC Curves [AUC]?=?0.626, P?=?0.003), especially for non-smokers (AUC?=?0.686, P?=?0.023). CONCLUSIONS: We identified an eight-miRNA signature that is prognostic of LUAD. The miRNA signature, if validated in other prospective studies, may have important implications in clinical practice, in particular identifying a subgroup of patients with LUAD who are at high risk of mortality.
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Project description:The oral microbiome can play a role in the instigation and progression of oral diseases that can manifest into other systemic conditions. These associations encourage the exploration of oral dysbiosis leading to the pathogenesis of cancers. In this study, oral rinse was used to characterize the oral microbiome fluctuation associated with oral cavity cancer (OCC) and oropharyngeal cancers (OPC). The study cohort consists of normal healthy controls (n = 10, between 20 and 30 years of age; n = 10, above 50 years of age), high-risk individuals (n = 11, above 50 years of age with bad oral hygiene and/or oral diseases) and OCC and OPC patients (n = 31, HPV-positive; n = 21, HPV-negative). Oral rinse samples were analyzed using 16S rRNA gene amplicon sequencing on the MiSeq platform. Kruskal-Wallis rank test was used to identify genera associated with OCC and OPC. A logistic regression analysis was carried out to determine the performance of these genera as a biomarker panel to predict OCC and OPC. In addition, a two-fold cross-validation with a bootstrap procedure was carried out in R to investigate how well the panel would perform in an emulated clinical scenario. Our data indicate that the oral microbiome is able to predict the presence of OCC and OPC with sensitivity and specificity of 100 and 90%, respectively. With further validation, the panel could potentially be implemented into clinical diagnostic and prognostic workflows for OCC and OPC.
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