scholarly journals RNA-Sequencing Data Reveal a Prognostic Four-lncRNA-Based Risk Score for Bladder Urothelial Carcinoma: An in Silico Update

2018 ◽  
Vol 50 (4) ◽  
pp. 1474-1495 ◽  
Author(s):  
Rong-Quan He ◽  
Zhi-Guang Huang ◽  
Tian-Yu Li ◽  
Yan-Ping Wei ◽  
Gang Chen ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Clinical Application ◽  
Risk Score ◽  
In Silico ◽  
Differential Expression Analysis ◽  
Cox Proportional Hazards ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Sequencing Data ◽  
Bioinformatics Analyses

Background/Aims: Current practical advances in high-throughput data technologies including RNA-sequencing have led to the identification of long non-coding RNAs (lncRNAs) for potential clinical application against bladder urothelial cancer (BLCA). However, most previous studies focused on the clinical value of individual lncRNAs, which has limited the potential for future clinical application. Methods: In this study, RNA-sequencing data of lncRNAs was downloaded from The Cancer Genome Atlas database. Risk score was constructed based on survival-associated lncRNAs identified using differential expression analysis as well as univariate and multivariate Cox proportional hazards regression analysis. Receiver operating characteristic and Kaplan-Meier curve analyses were employed to evaluate the clinical and prognostic value of risk scores. Bioinformatics analyses were used to investigate the potential mechanisms of newly identified lncRNAs. Results: Among 2,127 differentially expressed lncRNAs (DELs), four new lncRNAs (AC145124.1, AC010168.2, MIR200CHG, and AC098613.1) showed valuable prognostic effects in BLCA patients. More importantly, the four-DEL-based risk score had the potential to become an independent marker for the survival status prediction of BLCA patients. Distinct co-expressed genes and signaling pathways were identified when BLCA was categorized into low- and high-risk groups. Furthermore, a protein-coding gene, HIST4H4 was found only 68 bp from the AC010168.2 DEL. HIST4H4 expression level was evidently up-regulated and positively correlated with AC010168.2 in BLCA patients. Conclusion: This in silico investigation pioneers the future investigation of the utility of prognostic lncRNAs for BLCA.

scholarly journals A Novel Risk-Score Model With Eight MiRNA Signatures for Overall Survival of Patients With Lung Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Jun Wu ◽  
Yuqing Lou ◽  
Yi-Min Ma ◽  
Jun Xu ◽  
Tieliu Shi
Keyword(s):  
Overall Survival ◽  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Early Stage ◽  
Differential Expression Analysis ◽  
The Cancer Genome Atlas ◽  
Clinical Staging ◽  
Risk Scores ◽  
Regulatory Relationship ◽  
Score Model

Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer with heterogeneous outcomes and diverse therapeutic responses. To classify patients into different groups and facilitate the suitable therapeutic strategy, we first selected eight microRNA (miRNA) signatures in The Cancer Genome Atlas (TCGA)-LUAD cohort based on multi-strategy combination, including differential expression analysis, regulatory relationship, univariate survival analysis, importance clustering, and multivariate combinations analysis. Using the eight miRNA signatures, we further built novel risk scores based on the predefined cutoff and beta coefficients and divided the patients into high-risk and low-risk groups with significantly different overall survival time (p-value < 2 e−16). The risk-score model was confirmed with an independent dataset (p-value = 4.71 e−4). We also observed that the risk scores of early-stage patients were significantly lower than those of late-stage patients. Moreover, our model can also provide new insights into the current clinical staging system and can be regarded as an alternative system for patient stratification. This model unified the variable value as the beta coefficient facilitating the integration of biomarkers obtained from different omics data.

scholarly journals Development of a Four-mRNA Expression-Based Prognostic Signature for Cutaneous Melanoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Haiya Bai ◽  
Youliang Wang ◽  
Huimin Liu ◽  
Junyang Lu
Keyword(s):  
Rna Sequencing ◽  
Cutaneous Melanoma ◽  
Test Group ◽  
Training Group ◽  
Cox Proportional Hazards ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Learning Approaches ◽  
Prognostic Signature ◽  
Sequencing Data

We aim to find a biomarker that can effectively predict the prognosis of patients with cutaneous melanoma (CM). The RNA sequencing data of CM was downloaded from The Cancer Genome Atlas (TCGA) database and randomly divided into training group and test group. Survival statistical analysis and machine-learning approaches were performed on the RNA sequencing data of CM to develop a prognostic signature. Using univariable Cox proportional hazards regression, random survival forest algorithm, and receiver operating characteristic (ROC) in the training group, the four-mRNA signature including CD276, UQCRFS1, HAPLN3, and PIP4P1 was screened out. The four-mRNA signature could divide patients into low-risk and high-risk groups with different survival outcomes (log-rank p < 0.001). The predictive efficacy of the four-mRNA signature was confirmed in the test group, the whole TCGA group, and the independent GSE65904 (log-rank p < 0.05). The independence of the four-mRNA signature in prognostic prediction was demonstrated by multivariate Cox analysis. ROC and timeROC analyses showed that the efficiency of the signature in survival prediction was better than other clinical variables such as melanoma Clark level and tumor stage. This study highlights that the four-mRNA model could be used as a prognostic signature for CM patients with potential clinical application value.

scholarly journals HEDGEHOG/GLI Modulates the PRR11-SKA2 Bidirectional Transcription Unit in Lung Squamous Cell Carcinomas

Genes ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 120
Author(s):  
Yiyun Sun ◽  
Dandan Xu ◽  
Chundong Zhang ◽  
Yitao Wang ◽  
Lian Zhang ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Squamous Cell ◽  
Gene Pair ◽  
Gene List ◽  
Ectopic Expression ◽  
Lung Squamous Cell Carcinoma ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Gene Set

We previously demonstrated that proline-rich protein 11 (PRR11) and spindle and kinetochore associated 2 (SKA2) constituted a head-to-head gene pair driven by a prototypical bidirectional promoter. This gene pair synergistically promoted the development of non-small cell lung cancer. However, the signaling pathways leading to the ectopic expression of this gene pair remains obscure. In the present study, we first analyzed the lung squamous cell carcinoma (LSCC) relevant RNA sequencing data from The Cancer Genome Atlas (TCGA) database using the correlation analysis of gene expression and gene set enrichment analysis (GSEA), which revealed that the PRR11-SKA2 correlated gene list highly resembled the Hedgehog (Hh) pathway activation-related gene set. Subsequently, GLI1/2 inhibitor GANT-61 or GLI1/2-siRNA inhibited the Hh pathway of LSCC cells, concomitantly decreasing the expression levels of PRR11 and SKA2. Furthermore, the mRNA expression profile of LSCC cells treated with GANT-61 was detected using RNA sequencing, displaying 397 differentially expressed genes (203 upregulated genes and 194 downregulated genes). Out of them, one gene set, including BIRC5, NCAPG, CCNB2, and BUB1, was involved in cell division and interacted with both PRR11 and SKA2. These genes were verified as the downregulated genes via RT-PCR and their high expression significantly correlated with the shorter overall survival of LSCC patients. Taken together, our results indicate that GLI1/2 mediates the expression of the PRR11-SKA2-centric gene set that serves as an unfavorable prognostic indicator for LSCC patients, potentializing new combinatorial diagnostic and therapeutic strategies in LSCC.

scholarly journals Effect of Different Expression of Immune-Related lncRNA on Colon Adenocarcinoma and Its Relation to Prognosis

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Meiwei Mu ◽  
Yi Tang ◽  
Zheng Yang ◽  
Yuling Qiu ◽  
Xiaohong Li ◽  
...  
Keyword(s):  
Cox Regression ◽  
Colon Adenocarcinoma ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Future Research ◽  
Sequencing Data ◽  
Tissue Samples ◽  
Gene Sets ◽  
Cancer Genome Atlas ◽  
Immune Related Genes

Objective. To explore the expression of immune-related lncRNAs in colon adenocarcinoma and find out the effect on how these lncRNAs influence the development and prognosis of colon adenocarcinoma. Method. Transcriptome data of colon adenocarcinoma from The Cancer Genome Atlas (TCGA) were downloaded, and gene sets “IMMUNE RESPONSE” and “IMMUNE SYSTEM PROCESS” were sought from the Molecular Signatures Database (MSigDB). The expression of immune-related genes was extracted that were immune-related mRNAs. Then, the immune-related lncRNAs were sought out by utilizing of the above data. Clinical traits were combined with immune-related lncRNAs, so that prognostic-related lncRNAs were identified by Cox regression. Multivariate Cox regression was built to calculate risk scores. Relationships between clinical traits and immune-related lncRNAs were also calculated. Result. A total of 480 colorectal adenocarcinoma patients and 41 normal control patients’ transcriptome sequencing data of tissue samples were obtained from TCGA database. 918 immune-related lncRNAs were screened. Cox regression showed that 34 immune-related lncRNAs were associated with colon adenocarcinoma prognosis. Seven lncRNAs were independent risk factors. Conclusion. This study revealed that some lncRNAs can affect the development and prognosis of colon adenocarcinoma. It may provide new theory evidence of molecular mechanism for the future research and molecular targeted therapy of colon adenocarcinoma.

A Six CT83-Related Genes Based Prognostic Signature for Lung Adenocarcinoma

2021 ◽  
Vol 24 ◽  
Author(s):  
Yongmei Wang ◽  
Guimin Zhang ◽  
Ruixian Wang
Keyword(s):  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Differential Expression ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Differential Expression Analysis ◽  
Functional Enrichment ◽  
Risk Scores ◽  
Cox Regression Analysis

Background: This study aims to explore the prognostic values of CT83 and CT83-related genes in lung adenocarcinoma (LUAD). Methods: We downloaded the mRNA profiles of 513 LUAD patients (RNA sequencing data) and 246 NSCLC patients (Affymetrix Human Genome U133 Plus 2.0 Array) from TCGA and GEO databases. According to the median expression of CT83, the TCGA samples were divided into high and low expression groups, and differential expression analysis between them was performed. Functional enrichment analysis of differential expression genes (DEGs) was conducted. Univariate Cox regression analysis and LASSO Cox regression analysis were performed to screen the optimal prognostic DEGs. Then we established the prognostic model. A Nomogram model was constructed to predict the overall survival (OS) probability of LUAD patients. Results: CT83 expression was significantly correlated to the prognosis of LUAD patients. A total of 59 DEGs were identified, and a predictive model was constructed based on six optimal CT83-related DEGs, including CPS1, RHOV, TNNT1, FAM83A, IGF2BP1, and GRIN2A, could effectively predict the prognosis of LUAD patients. The nomogram could reliably predict the OS of LUAD patients. Moreover, the six important immune checkpoints (CTLA4, PD1, IDO1, TDO2, LAG3, and TIGIT) were closely correlated with the Risk Score, which was also differentially expressed between the LUAD samples with high and low-Risk Scores, suggesting that the poor prognosis of LUAD patients with high-Risk Score might be due to the immunosuppressive microenvironments. Conclusion: A prognostic model based on six optimal CT83 related genes could effectively predict the prognosis of LUAD patients.

scholarly journals Identification of Key Genes and Pathways Associated With Paclitaxel Resistance in Esophageal Squamous Cell Carcinoma Based on Bioinformatics Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhimin Shen ◽  
Mingduan Chen ◽  
Fei Luo ◽  
Hui Xu ◽  
Peipei Zhang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Differential Expression Analysis ◽  
Biological Significance ◽  
The Cancer Genome Atlas ◽  
Paclitaxel Resistance ◽  
Bioinformatics Analyses ◽  
Key Genes

Esophageal squamous cell carcinoma (ESCC) ranks as the fourth leading cause of cancer-related death in China. Although paclitaxel has been shown to be effective in treating ESCC, the prolonged use of this chemical will lead to paclitaxel resistance. In order to uncover genes and pathways driving paclitaxel resistance in the progression of ESCC, bioinformatics analyses were performed based on The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database including GSE86099 and GSE161533. Differential expression analysis was performed in TCGA data and two GEO datasets to obtain differentially expressed genes (DEGs). Based on GSE161533, weighted gene co-expression network analysis (WGCNA) was conducted to identify the key modules associated with ESCC tumor status. The DEGs common to the two GEO datasets and the genes in the key modules were intersected to obtain the paclitaxel resistance-specific or non-paclitaxel resistance-specific genes, which were subjected to subsequent least absolute shrinkage and selection operator (LASSO) feature selection, whereby paclitaxel resistance-specific or non-paclitaxel resistance-specific key genes were selected. Ten machine learning models were used to validate the biological significance of these key genes; the potential therapeutic drugs for paclitaxel resistance-specific genes were also predicted. As a result, we identified 24 paclitaxel resistance-specific genes and 18 non-paclitaxel resistance-specific genes. The ESCC machine classifiers based on the key genes achieved a relatively high AUC value in the cross-validation and in an independent test set, GSE164158. A total of 207 drugs (such as bevacizumab) were predicted to be alternative therapeutics for ESCC patients with paclitaxel resistance. These results might shed light on the in-depth research of paclitaxel resistance in the context of ESCC progression.

In Silico Analysis of Micro-RNA Sequencing Data

2021 ◽  
pp. 231-251
Author(s):  
Ernesto Aparicio-Puerta ◽  
Bastian Fromm ◽  
Michael Hackenberg ◽  
Marc K. Halushka
Keyword(s):  
Rna Sequencing ◽  
In Silico ◽  
In Silico Analysis ◽  
Micro Rna ◽  
Sequencing Data ◽  
Silico Analysis

scholarly journals Genetic analysis of the molecular regulation of electric fields-guided glia migration

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Li Yao ◽  
Teresa Shippy ◽  
Yongchao Li
Keyword(s):  
Cell Migration ◽  
Rna Sequencing ◽  
Electric Fields ◽  
Differential Expression Analysis ◽  
Cell Types ◽  
Molecular Regulation ◽  
Sequencing Data ◽  
Oligodendrocyte Precursor ◽  
Go Terms ◽  
Developing Nervous System

Abstract In a developing nervous system, endogenous electric field (EF) influence embryonic growth. We reported the EF-directed migration of both rat Schwann cells (SCs) and oligodendrocyte precursor cells (OPCs) and explored the molecular mechanism using RNA-sequencing assay. However, previous studies revealed the differentially expressed genes (DEGs) associated with EF-guided migration of SCs or OPCs alone. In this study, we performed joint differential expression analysis on the RNA-sequencing data from both cell types. We report a number of significantly enriched gene ontology (GO) terms that are related to the cytoskeleton, cell adhesion, and cell migration. Of the DEGs associated with these terms, nine up-regulated DEGs and 32 down-regulated DEGs showed the same direction of effect in both SCs and OPCs stimulated with EFs, while the remaining DEGs responded differently. Thus, our study reveals the similarities and differences in gene expression and cell migration regulation of different glial cell types in response to EF stimulation.

scholarly journals Hypoxic Roadmap of Glioblastoma—Learning about Directions and Distances in the Brain Tumor Environment

Cancers ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1213 ◽  
Author(s):  
Agnieszka Bronisz ◽  
Elżbieta Salińska ◽  
E. Antonio Chiocca ◽  
Jakub Godlewski
Keyword(s):  
Brain Tumor ◽  
Immune Escape ◽  
Differential Expression Analysis ◽  
Metabolic Stress ◽  
The Cancer Genome Atlas ◽  
Adaptation To Hypoxia ◽  
Sequencing Data ◽  
Immune Therapies ◽  
Tumor Environment ◽  
Cancer Genome Atlas

Malignant brain tumor—glioblastoma is not only difficult to treat but also hard to study and model. One of the reasons for these is their heterogeneity, i.e., individual tumors consisting of cancer cells that are unlike each other. Such diverse cells can thrive due to the simultaneous co-evolution of anatomic niches and adaption into zones with distorted homeostasis of oxygen. It dampens cytotoxic and immune therapies as the response depends on the cellular composition and its adaptation to hypoxia. We explored what transcriptome reposition strategies are used by cells in the different areas of the tumor. We created the hypoxic map by differential expression analysis between hypoxic and cellular features using RNA sequencing data cross-referenced with the tumor’s anatomic features (Ivy Glioblastoma Atlas Project). The molecular functions of genes differentially expressed in the hypoxic regions were analyzed by a systematic review of the gene ontology analysis. To put a hypoxic niche signature into a clinical context, we associated the model with patients’ survival datasets (The Cancer Genome Atlas). The most unique class of genes in the hypoxic area of the tumor was associated with the process of autophagy. Both hypoxic and cellular anatomic features were enriched in immune response genes whose, along with autophagy cluster genes, had the power to predict glioblastoma patient survival. Our analysis revealed that transcriptome responsive to hypoxia predicted worse patients’ outcomes by driving tumor cell adaptation to metabolic stress and immune escape.

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