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Small Nucleolar RNAs (snoRNAs)-Based Risk Score Classifier Predicts Overall Survival in Bladder Carcinoma.

ABSTRACT: BACKGROUND Bladder carcinoma (BLCA) is a leading cause of cancer-related deaths worldwide. The aim of this work was to develop an accurate stratification in predicting the prognosis and directing the treatment of BLCA patients based on small nucleolar RNAs (snoRNAs). MATERIAL AND METHODS Expression profiles of snoRNAs were downloaded from the SNORic database. The expression profiles and clinical outcomes of BLCA patients were analyzed. Survival-associated snoRNAs were identified and used to develop a novel risk score classifier. Genes in the whole genome that were significantly correlated with the included prognostic snoRNAs were used for functional enrichment analysis. RESULTS The results showed that age, American Joint Committee on Cancer (AJCC) stage, and tumor status were significantly correlated with overall survival (OS) of BLCA patients. We selected 12 survival-associated snoRNAs to build a prognostic signature. Patients were separated into high- and low-risk groups based on the median value of the risk score. Patients in the high-risk group and low-risk group have distinct clinical outcomes. The AJCC TNM stage showed moderate utility as a prognostic indicator for clinical outcome prediction. Then, clinical parameters and risk scores were entered in multivariate Cox analysis. Notably, the prognostic signature remained an independent significant prognostic risk factor. The pathway analysis suggested that these genes were enriched in several types of cancer and "Focal adhesion" pathways. CONCLUSIONS The prognostic signature defined by expression profiles of 12 survival-associated snoRNAs appears to be an excellent predictor of the clinical outcome of BLCA patients.

SUBMITTER: He RQ

PROVIDER: S-EPMC7597584 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Small Nucleolar RNAs (snoRNAs)-Based Risk Score Classifier Predicts Overall Survival in Bladder Carcinoma.

He Rong-Quan RQ Huang Zhi-Guang ZG Zhai Gao-Qiang GQ Huang Su-Ning SN Gu Yong-Yao YY Gu Yong-Yao YY Chen Gang G Ma Jie J Cheng Ji-Wen JW Yan Hai-Biao HB Li Sheng-Hua SH

Medical science monitor : international medical journal of experimental and clinical research 20201026

BACKGROUND Bladder carcinoma (BLCA) is a leading cause of cancer-related deaths worldwide. The aim of this work was to develop an accurate stratification in predicting the prognosis and directing the treatment of BLCA patients based on small nucleolar RNAs (snoRNAs). MATERIAL AND METHODS Expression profiles of snoRNAs were downloaded from the SNORic database. The expression profiles and clinical outcomes of BLCA patients were analyzed. Survival-associated snoRNAs were identified and used to deve ...[more]

PMID: 33104528

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Project description:BackgroundRecently, a newly programmed cell death has been discovered, namely cuproptosis. It is considered a novel copper-dependent cell death model. Long non-coding RNA (lncRNA) influence the prognosis of bladder cancer. In this study, we established a scoring system based on 7 cuproptosis-related lncRNA to predict the prognosis and immune landscape of bladder cancer (BCa).MethodGene expression and clinical data of 431 tissues were downloaded from The Cancer Genome Atlas (TCGA), including 19 normal samples and 419 cancer samples. All samples were randomly categorized into train and test cohorts. Cuproptosis-related lncRNA were distinguished. Then we conduct univariate COX and multivariate COX regression, paralleled with LASSO regression to cultivate a cuproptosis-related lncRNA risk model. Kaplan-Meier curves, scatter diagram, C-index, ROC curves, nomogram, PCA analysis and univariate and multivariate Cox regression were used to test the accuracy of risk model and to predict patient survival. Additional, gene mutation status between high- and low-risk groups was calculated.GO and KEGG were used to access the DEGs (different expression genes)-related pathway.The ssGSEA and ESTIMATE algorithms were used to assess the immune function in different tumor samples. Besides, patient's response to immunotherapy and drug susceptibility were also been estimated.Results7 cuproptosis-related lncRNA (LINC01184, LINC00513, LINC02443, SMARCA5-AS1, BDNF-AS, SOD2-OT1, HYI-AS1) were selected to construct the risk model in the train cohort. This model can well predict the overall survival (OS) in test group and entire cohort with different stage. Despite no significant different is observed in gene mutation between high- and low-risk group, different immune infiltration, different survival and sensitivity to drugs are discovered.ConclusionWe established a novel cuproptosis-related lncRNA risk model which can predict the outcome and immunotherapy response with satisfactory predictive effects. This risk model can provide a new insight into prognostic evaluation and may have potential to guide comprehensive treatment in bladder cancer.

Project description:BackgroundPancreatic carcinoma is one of the most lethal human cancers. In patients with resectable tumors, surgery followed by adjuvant chemotherapy is the only curative treatment. However, the 5-year survival is 20%. Because of a strong metastatic propensity, neoadjuvant chemotherapy is being tested in randomized clinical trials. In this context, improving the selection of patients for immediate surgery or neoadjuvant chemotherapy is crucial, and high-throughput molecular analyses may help; the present study aims to address this.MethodsClinicopathological and gene expression data of 695 pancreatic carcinoma samples were collected from nine datasets and supervised analysis was applied to search for a gene expression signature predictive for overall survival (OS) in the 601 informative operated patients. The signature was identified in a learning set of patients and tested for its robustness in a large independent validation set.ResultsSupervised analysis identified 1400 genes differentially expressed between two selected patient groups in the learning set, namely 17 long-term survivors (LTS; ≥ 36 months after surgery) and 22 short-term survivors (STS; dead of disease between 2 and 6 months after surgery). From these, a 25-gene prognostic classifier was developed, which identified two classes ("STS-like" and "LTS-like") in the independent validation set (n = 562), with a 25% (95% CI 18-33) and 48% (95% CI 42-54) 2-year OS (P = 4.33 × 10-9), respectively. Importantly, the prognostic value of this classifier was independent from both clinicopathological prognostic features and molecular subtypes in multivariate analysis, and existed in each of the nine datasets separately. The generation of 100,000 random gene signatures by a resampling scheme showed the non-random nature of our prognostic classifier.ConclusionThis study, the largest prognostic study of gene expression profiles in pancreatic carcinoma, reports a 25-gene signature associated with post-operative OS independently of classical factors and molecular subtypes. This classifier may help select patients with resectable disease for either immediate surgery (the LTS-like class) or neoadjuvant chemotherapy (the STS-like class). Its assessment in the current prospective trials of adjuvant and neoadjuvant chemotherapy trials is warranted, as well as the functional analysis of the classifier genes, which may provide new therapeutic targets.

Project description:Abstract Background Diffuse large B‐cell lymphoma (DLBCL) is a non‐Hodgkin lymphoma with high mortality rates. Small nucleolar RNAs (snoRNAs) are tumor‐specific biological markers, but there are few studies on the role of snoRNAs in DLBCL. Materials and Methods Survival‐related snoRNAs were selected to construct a specific snoRNA‐based signature via computational analyses (Cox regression and independent prognostic analyses) to predict the prognosis of DLBCL patients. To assist in clinical applications, a nomogram was built by combining the risk model and other independent prognostic factors. Pathway analysis, gene ontology analysis, transcription factor enrichment, protein–protein interactions, and single nucleotide variant analysis were used to explore the potential biological mechanisms of co‐expressed genes. Results Twelve prognosis‐correlated snoRNAs were selected from the DLBCL patient cohort of microarray profiles, and a three‐snoRNA signature consisting of SNORD1A, SNORA60, and SNORA66 was constructed. DLBCL patients could be divided into high‐risk and low‐risk cohorts using the risk model, and the high‐risk group and activated B cell‐like (ABC) type DLBCL were linked with disappointing survival. In addition, SNORD1A co‐expressed genes were inseparably linked to the biological functions of the ribosome and mitochondria. Potential transcriptional regulatory networks have also been identified. MYC and RPL10A were the most mutated SNORD1A co‐expressed genes in DLBCL. Conclusion Put together, our findings explored the potential biological effects of snoRNAs in DLBCL, and provided a new predictor for DLBCL prediction. A comprehensive analysis was performed to select significant survival‐related snoRNAs in DLBCL. Collectively, a prognostic signature based on three snoRNAs (SNORD1A, SNORA60, and SNORA66) was established for DLBCL patients. Also, we found snoRNA co‐expressed genes that were mostly involved in ribosome‐correlated pathways and processes, and the interaction of ribosomal proteins (RPL23, RPL10A) and p53.

Project description:BackgroundThe fatality and recurrence rates of bladder cancer (BC) have progressively increased. DNA methylation is an influential regulator associated with gene transcription in the pathogenesis of BC. We describe a comprehensive epigenetic study performed to analyse DNA methylation-driven genes in BC.MethodsData related to DNA methylation, the gene transcriptome and survival in BC were downloaded from The Cancer Genome Atlas (TCGA). MethylMix was used to detect BC-specific hyper-/hypo-methylated genes. Metascape was used to carry out gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. A least absolute shrinkage and selection operator (LASSO)-penalized Cox regression was conducted to identify the characteristic dimension decrease and distinguish prognosis-related methylation-driven genes. Subsequently, we developed a six-gene risk evaluation model and a novel prognosis-related nomogram to predict overall survival (OS). A survival analysis was carried out to explore the individual prognostic significance of the six genes.ResultsIn total, 167 methylation-driven genes were identified. Based on the LASSO Cox regression, six genes, i.e., ARHGDIB, LINC00526, IDH2, ARL14, GSTM2, and LURAP1, were selected for the development of a risk evaluation model. The Kaplan-Meier curve indicated that patients in the low-risk group had considerably better OS (P?=?1.679e-05). The area under the curve (AUC) of this model was 0.698 at 3 years of OS. The verification performed in subgroups demonstrated the validity of the model. Then, we designed an OS-associated nomogram that included the risk score and clinical factors. The concordance index of the nomogram was 0.694. The methylation levels of IDH2 and ARL14 were appreciably related to the survival results. In addition, the methylation and gene expression-matched survival analysis revealed that ARHGDIB and ARL14 could be used as independent prognostic indicators. Among the six genes, 6 methylation sites in ARHGDIB, 3 in GSTM2, 1 in ARL14, 2 in LINC00526 and 2 in LURAP1 were meaningfully associated with BC prognosis. In addition, several abnormal methylated sites were identified as linked to gene expression.ConclusionWe discovered differential methylation in BC patients with better and worse survival and provided a risk evaluation model by merging six gene markers with clinical characteristics.

Dataset Information

Small Nucleolar RNAs (snoRNAs)-Based Risk Score Classifier Predicts Overall Survival in Bladder Carcinoma.

Publications

Small Nucleolar RNAs (snoRNAs)-Based Risk Score Classifier Predicts Overall Survival in Bladder Carcinoma.

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