scholarly journals A Novel Model Based on Genomic Instability-Associated Long Non-Coding RNAs for Predicting Prognosis and Response to Immunotherapy in Patients With Lung Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Guangxu Tu ◽  
Weilin Peng ◽  
Qidong Cai ◽  
Zhenyu Zhao ◽  
Xiong Peng ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Genomic Instability ◽  
Prognostic Value ◽  
Predictive Accuracy ◽  
Expression Profiles ◽  
Pearson Correlation ◽  
Characteristic Curve ◽  
Prognostic Index ◽  
The Cancer Genome Atlas ◽  
Non Coding Rnas

Background: Emerging scientific evidence has shown that long non-coding RNAs (lncRNAs) exert critical roles in genomic instability (GI), which is considered a hallmark of cancer. To date, the prognostic value of GI-associated lncRNAs (GI-lncRNAs) remains largely unexplored in lung adenocarcinoma (LUAC). The aims of this study were to identify GI-lncRNAs associated with the survival of LUAC patients, and to develop a novel GI-lncRNA-based prognostic model (GI-lncRNA model) for LUAC.Methods: Clinicopathological data of LUAC patients, and their expression profiles of lncRNAs and somatic mutations were obtained from The Cancer Genome Atlas database. Pearson correlation analysis was conducted to identify the co-expressed mRNAs of GI-lncRNAs. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were conducted to determine the main biological function and molecular pathways of the differentially expressed GI-lncRNAs. Univariate and multivariate Cox proportional hazard regression analyses were performed to identify GI-lncRNAs significantly related to overall survival (OS) for construction of the GI-lncRNA model. Kaplan–Meier survival analysis and receiver operating characteristic curve analysis were performed to evaluate the predictive accuracy. The performance of the newly developed GI-lncRNA model was compared with the recently published lncRNA-based prognostic index models.Results: A total of 19 GI-lncRNAs were found to be significantly associated with OS, of which 9 were identified by multivariate analysis to construct the GI-lncRNA model. Notably, the GI-lncRNA model showed a prognostic value independent of key clinical characteristics. Further performance evaluation indicated that the area under the curve (AUC) of the GI-lncRNA model was 0.771, which was greater than that of the TP53 mutation status and three existing lncRNA-based models in predicting the prognosis of patients with LUAC. In addition, the GI-lncRNA model was highly correlated with programed death ligand 1 (PD-L1) expression and tumor mutational burden in immunotherapy for LUAC.Conclusion: The GI-lncRNA model was established and its performance was found to be superior to existing lncRNA-based models. As such, the GI-lncRNA model holds promise as a more accurate prognostic tool for the prediction of prognosis and response to immunotherapy in patients with LUAC.

scholarly journals An interactive network of alternative splicing events with prognostic value in geriatric lung adenocarcinoma via the regulation of splicing factors

2020 ◽  
Vol 40 (10) ◽  
Author(s):  
Yidi Wang ◽  
Yaxuan Wang ◽  
Kenan Li ◽  
Yabing Du ◽  
Kang Cui ◽  
...  
Keyword(s):  
Alternative Splicing ◽  
Lung Adenocarcinoma ◽  
Prognostic Value ◽  
Prognostic Index ◽  
Clinical Information ◽  
The Cancer Genome Atlas ◽  
Splicing Factors ◽  
Systematic Analysis ◽  
Interactive Network ◽  
Underlying Mechanisms

Abstract Alternative splicing (AS), an essential process for the maturation of mRNAs, is involved in tumorigenesis and tumor progression, including angiogenesis, apoptosis, and metastasis. AS changes can be frequently observed in different tumors, especially in geriatric lung adenocarcinoma (GLAD). Previous studies have reported an association between AS events and tumorigenesis but have lacked a systematic analysis of its underlying mechanisms. In the present study, we obtained splicing event information from SpliceSeq and clinical information regarding GLAD from The Cancer Genome Atlas. Survival-associated AS events were selected to construct eight prognostic index (PI) models. We also constructed a correlation network between splicing factors (SFs) and survival-related AS events to identify a potential molecular mechanism involved in regulating AS-related events in GLAD. Our study findings confirm that AS has a strong prognostic value for GLAD and sheds light on the clinical significance of targeting SFs in the treatment of GLAD.

scholarly journals Identification and validation of a prognostic index based on a metabolic-genomic landscape analysis of ovarian cancer

2019 ◽  
Author(s):  
Zi-yao Wang ◽  
Yukun Li ◽  
Gui-fang Luo ◽  
Juan Zou ◽  
Chang-ye Chen ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Prognostic Value ◽  
Expression Profiles ◽  
Prognostic Index ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Lasso Regression ◽  
Metabolic System ◽  
Cancer Genome Atlas ◽  
Personalized Cancer

Abstract Purpose: Tumor metabolism has been a novel driver of personalized cancer medicine, with aggressive efforts to regulate the metabolic system to prolong their life. The aim of this study is to explore the prognostic value of metabolism in ovarian serous cystadenocarcinoma (OSC), which is the most common subtype of ovarian cancer, accounts for 75-80% of reported cases. Patients and methods: we integrated the expression profiles of metabolism-related genes (MRGs) in survival in 379 OSC patients based on The Cancer Genome Atlas (TCGA) database. Then, several biomedical computational algorithms were employed to identify eight key prognostic MRGs, which were related with overall survival (OS) significantly in OSC. The eight genes represented important clinical significance and prognostic value in OSC. Then a prognostic index was constructed. Results: A total of 701 differentially expressed metabolism-related genes (MRGs) were identified in OSC patients based on TCGA database. Functional enrichment analyses hinted that metabolism may act in a significant role in the development and progression of OSC. Random walking with restart (RWR) algorithm, Univariate cox and lasso regression analysis indicated a prognostic signature based on MRGs (ENPP1, FH, CYP2E1, HPGDS, ADCY9, NDUFA5, ADH1B and PYGB), which performed moderately in prognostic predictions. Conclusion: This study provides a latent prognostic feature for predicting the prognosis of OSC patients and the molecular mechanism of OSC metabolism.

scholarly journals Clinical Prognosis of Keratin Family Genes in Patients with LUAD a Study Based on TCGA Database

2021 ◽  
Author(s):  
Xia Liu ◽  
Hangzhou Zhu ◽  
Xiaojiu Zha ◽  
Yan Rui ◽  
Miao Li ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Cause Of Death ◽  
Prognostic Value ◽  
Cox Regression ◽  
Characteristic Curve ◽  
The Cancer Genome Atlas ◽  
Clinical Prognosis ◽  
Cox Regression Analysis

Abstract Background: Malignant tumor is the main cause of death in the world, among which lung cancer is the main cause of death. The incidence rate and mortality of lung cancer are increasing year by year. This study aims to elucidate the potential prognostic value of keratin (KRT) gene family members in patients with lung adenocarcinoma (LUAD).Materials and methods: RNA sequencing data were obtained from the Cancer Genome Atlas (TCGA) database of LUAD tumors and paired normal tissues. Multivariate Cox proportional hazards regression analysis was used to evaluate the prognostic value of KRT family member genes. Analyze the screening variables to construct the risk score. The time-dependent ROC curve is used to evaluate the predicted results. Finally, nomograms were used to assess individualized prognostic risk.Result: From the differentially expressed genes, 14 KRT genes with significant imbalance in LUAD tumors and adjacent non-cancerous tissues were screened. Receiver operating characteristic curve (ROC) analysis confirmed that these 14 KRT genes can be used as potential diagnostic markers for the diagnosis of lung adenocarcinoma. Multivariate Cox regression analysis showed that six KRT genes were related to the prognosis of lung cancer. The variables were screened by multivariate Cox regression model. The final results showed that KRT8 and KRT6A were independent risk factors for the prognosis of lung adenocarcinoma.Conclusion: KRT8 and KRT6A can be used as prognostic markers of LUAD. The high expression of KRT8 and KRT6A suggests that the prognosis of LUAD patients is poor.

scholarly journals Circulating long non-coding RNAs HOTAIR, Linc-p21, GAS5 and XIST expression profiles in diffuse large B-cell lymphoma: association with R-CHOP responsiveness

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mahmoud A. Senousy ◽  
Aya M. El-Abd ◽  
Raafat R. Abdel-Malek ◽  
Sherine M. Rizk
Keyword(s):  
B Cell ◽  
Cell Lymphoma ◽  
Expression Profiles ◽  
International Prognostic Index ◽  
Prognostic Index ◽  
B Cell Lymphoma ◽  
Diagnostic Tools ◽  
Large B Cell Lymphoma ◽  
Non Coding Rnas ◽  
Large B Cell

AbstractThe reliable identification of diffuse large B-cell lymphoma (DLBCL)-specific targets owns huge implications for its diagnosis and treatment. Long non-coding RNAs (lncRNAs) are implicated in DLBCL pathogenesis; however, circulating DLBCL-related lncRNAs are barely investigated. We investigated plasma lncRNAs; HOTAIR, Linc-p21, GAS5 and XIST as biomarkers for DLBCL diagnosis and responsiveness to R-CHOP therapy. Eighty-four DLBCL patients and thirty-three healthy controls were included. Only plasma HOTAIR, XIST and GAS5 were differentially expressed in DLBCL patients compared to controls. Pretreatment plasma HOTAIR was higher, whereas GAS5 was lower in non-responders than responders to R-CHOP. Plasma GAS5 demonstrated superior diagnostic accuracy (AUC = 0.97) whereas a panel of HOTAIR + GAS5 superiorly discriminated responders from non-responders by ROC analysis. In multivariate analysis, HOTAIR was an independent predictor of non-response. Among patients, plasma HOTAIR, Linc-p21 and XIST were correlated. Plasma GAS5 negatively correlated with International Prognostic Index, whereas HOTAIR positively correlated with performance status, denoting their prognostic potential. We constructed the lncRNAs-related protein–protein interaction networks linked to drug response via bioinformatics analysis. In conclusion, we introduce plasma HOTAIR, GAS5 and XIST as potential non-invasive diagnostic tools for DLBCL, and pretreatment HOTAIR and GAS5 as candidates for evaluating therapy response, with HOTAIR as a predictor of R-CHOP failure. We provide novel surrogates for future predictive studies in personalized medicine.

scholarly journals Integrative transcriptome data mining for identification of core lncRNAs in breast cancer

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7821 ◽  
Author(s):  
Xiaoming Zhang ◽  
Jing Zhuang ◽  
Lijuan Liu ◽  
Zhengguo He ◽  
Cun Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Prognostic Value ◽  
Early Stage ◽  
Expression Profiles ◽  
Diagnostic Value ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Data Set

Background Cumulative evidence suggests that long non-coding RNAs (lncRNAs) play an important role in tumorigenesis. This study aims to identify lncRNAs that can serve as new biomarkers for breast cancer diagnosis or screening. Methods First, the linear fitting method was used to identify differentially expressed genes from the breast cancer RNA expression profiles in The Cancer Genome Atlas (TCGA). Next, the diagnostic value of all differentially expressed lncRNAs was evaluated using a receiver operating characteristic (ROC) curve. Then, the top ten lncRNAs with the highest diagnostic value were selected as core genes for clinical characteristics and prognosis analysis. Furthermore, core lncRNA-mRNA co-expression networks based on weighted gene co-expression network analysis (WGCNA) were constructed, and functional enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The differential expression level and diagnostic value of core lncRNAs were further evaluated by using independent data set from Gene Expression Omnibus (GEO). Finally, the expression status and prognostic value of core lncRNAs in various tumors were analyzed based on Gene Expression Profiling Interactive Analysis (GEPIA). Results Seven core lncRNAs (LINC00478, PGM5-AS1, AL035610.1, MIR143HG, RP11-175K6.1, AC005550.4, and MIR497HG) have good single-factor diagnostic value for breast cancer. AC093850.2 has a prognostic value for breast cancer. AC005550.4 and MIR497HG can better distinguish breast cancer patients in early-stage from the advanced-stage. Low expression of MAGI2-AS3, LINC00478, AL035610.1, MIR143HG, and MIR145 may be associated with lymph node metastasis in breast cancer. Conclusion Our study provides candidate biomarkers for the diagnosis and prognosis of breast cancer, as well as a bioinformatics basis for the further elucidation of the molecular pathological mechanism of breast cancer.

scholarly journals Construction and validation of a novel prognostic signature of microRNAs in lung adenocarcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10470
Author(s):  
Wanzhen Li ◽  
Shiqing Liu ◽  
Shihong Su ◽  
Yang Chen ◽  
Gengyun Sun
Keyword(s):  
Lung Adenocarcinoma ◽  
Signaling Pathway ◽  
Prognostic Value ◽  
Target Genes ◽  
Predictive Accuracy ◽  
Differentially Expressed ◽  
Prognostic Signature ◽  
Tissue Samples ◽  
Gene Set ◽  
Differentially Expressed Mirnas

MicroRNA (miRNA, miR) has been reported to be highly implicated in a wide range of biological processes in lung cancer (LC), and identification of differentially expressed miRNAs between normal and LC samples has been widely used in the discovery of prognostic factors for overall survival (OS) and response to therapy. The present study was designed to develop and evaluate a miRNA-based signature with prognostic value for the OS of lung adenocarcinoma (LUAD), a common histologic subtype of LC. In brief, the miRNA expression profiles and clinicopathological factors of 499 LUAD patients were collected from The Cancer Genome Atlas (TCGA) database. Kaplan–Meier (K-M) survival analysis showed significant correlations between differentially expressed miRNAs and LUAD survival outcomes. Afterward, 1,000 resample LUAD training matrices based on the training set was applied to identify the potential prognostic miRNAs. The least absolute shrinkage and selection operator (LASSO) cox regression analysis was used to constructed a six-miRNA based prognostic signature for LUAD patients. Samples with different risk scores displayed distinct OS in K-M analysis, indicating considerable predictive accuracy of this signature in both training and validation sets. Furthermore, time-dependent receiver operating characteristic (ROC) analysis demonstrated the nomogram achieved higher predictive accuracy than any other clinical variables after incorporating the clinical information (age, sex, stage, and recurrence). In the stratification analysis, the prognostic value of this classifier in LUAD patients was validated to be independent of other clinicopathological variables, such as age, gender, tumor recurrence, and early stage. Gene set annotation analyses were also conducted through the Hallmark gene set and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, indicating target genes of the six miRNAs were positively related to various molecular pathways of cancer, such as hallmark UV response, Wnt signaling pathway and mTOR signaling pathway. In addition, fresh cancer tissue samples and matched adjacent tissue samples from 12 LUAD patients were collected to verify the expression of miR-582’s target genes in the model, further revealing the potential relationship between SOX9, RASA1, CEP55, MAP4K4 and LUAD tumorigenesis, and validating the predictive value of the model. Taken together, the present study identified a robust signature for the OS prediction of LUAD patients, which could potentially aid in the individualized selection of therapeutic approaches for LUAD patients.

scholarly journals Predicting lung adenocarcinoma disease progression using methylation-correlated blocks and ensemble machine learning classifiers

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10884
Author(s):  
Xin Yu ◽  
Qian Yang ◽  
Dong Wang ◽  
Zhaoyang Li ◽  
Nianhang Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Characteristic Curve ◽  
The Cancer Genome Atlas ◽  
Support Vector ◽  
Survival Prediction ◽  
Ensemble Model ◽  
Training Set ◽  
Cpg Sites

Applying the knowledge that methyltransferases and demethylases can modify adjacent cytosine-phosphorothioate-guanine (CpG) sites in the same DNA strand, we found that combining multiple CpGs into a single block may improve cancer diagnosis. However, survival prediction remains a challenge. In this study, we developed a pipeline named “stacked ensemble of machine learning models for methylation-correlated blocks” (EnMCB) that combined Cox regression, support vector regression (SVR), and elastic-net models to construct signatures based on DNA methylation-correlated blocks for lung adenocarcinoma (LUAD) survival prediction. We used methylation profiles from the Cancer Genome Atlas (TCGA) as the training set, and profiles from the Gene Expression Omnibus (GEO) as validation and testing sets. First, we partitioned the genome into blocks of tightly co-methylated CpG sites, which we termed methylation-correlated blocks (MCBs). After partitioning and feature selection, we observed different diagnostic capacities for predicting patient survival across the models. We combined the multiple models into a single stacking ensemble model. The stacking ensemble model based on the top-ranked block had the area under the receiver operating characteristic curve of 0.622 in the TCGA training set, 0.773 in the validation set, and 0.698 in the testing set. When stratified by clinicopathological risk factors, the risk score predicted by the top-ranked MCB was an independent prognostic factor. Our results showed that our pipeline was a reliable tool that may facilitate MCB selection and survival prediction.

scholarly journals TCR Clonality and Genomic Instability Signatures as Prognostic Biomarkers in High Grade Serous Ovarian Cancer

Cancers ◽  
2021 ◽  
Vol 13 (17) ◽  
pp. 4394
Author(s):  
Julie Lecuelle ◽  
Romain Boidot ◽  
Hugo Mananet ◽  
Valentin Derangère ◽  
Juliette Albuisson ◽  
...  
Keyword(s):  
T Cell ◽  
Genomic Instability ◽  
Survival Data ◽  
Prognostic Value ◽  
Prognostic Biomarker ◽  
Copy Number Variant ◽  
Cell Receptor ◽  
The Cancer Genome Atlas ◽  
High Grade ◽  
Cell Infiltrate

Purpose: Immune infiltration is a prognostic factor in high-grade serous ovarian carcinoma (HGSC) but immunotherapy efficacy is disappointing. Genomic instability is now used to guide the therapeutic value of PARP inhibitors. We aimed to investigate exome-derived parameters to assess the tumor microenvironment according to genomic instability profile. Methods: We used the HGSC TCGA (the cancer genome atlas) dataset with genomic characteristics, including homologous recombination deficiency (HRD), copy number variant (CNV) signatures, TCR (T cell receptor) clonality and abundance of tissue-infiltrating immune and stromal cell populations. We then investigated the relationship with survival data. Results: In 578 HGSC patients, HRD status, CNV signature 7 and TCR clonality were associated with longer survival. The combination of high CNV signature 7 expression and HRD status or high CNV signature 3 expression and high TCR clonality was associated with a trend towards longer survival compared to each variable alone. Combining T cell infiltrate and TCR clonality improved the prognostic value compared to T cells infiltration alone. Prognostic value of TCR clonality was confirmed in an independent cohort. Conclusions: TCR clonality is an emerging prognostic biomarker that improves T cell infiltrate information. Analysis of TCR clonality combined with genomic instability could be an interesting prognostic biomarker.

scholarly journals Identification and Validation of a Predictive Immune-related Genes Signature for the Prognosis of Cholangiocarcinoma

2020 ◽  
Author(s):  
Rui Zhang ◽  
Chen Chen ◽  
Qi Li ◽  
Jialu Fu ◽  
Dong Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
High Risk ◽  
Risk Score ◽  
Risk Model ◽  
Predictive Accuracy ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Immune Related Genes

Abstract Background: Immune-related genes (IRGs) play a crucial role in the initiation and progression of cholangiocarcinoma (CCA). However, immune signatures have rarely been used to predict prognosis of CCA. The aim of this study was to construct a novel model for CCA to predict survival based on IRGs expression data.Methods: The gene expression profiles and clinical data of CCA patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were integrated to establish and validate prognostic IRG signatures. Differentially expressed immune-related genes were screened. Univariate and multivariate Cox analysis were performed to identify prognostic IRGs, and the risk model that predicts outcomes was constructed. Furthermore, receiver operating characteristic (ROC) and Kaplan-Meier curve were plotted to examine predictive accuracy of the model, and a nomogram was constructed based on IRGs signature, combining with other clinical characteristics. Finally, CIBERSORT was used to analyze the association of immune cells infiltration with risk score.Results: We identified that 223 IRGs were significantly dysregulated in patients with CCA, among which five IRGs (AVPR1B, CST4, TDGF1, RAET1E and IL9R) were identified as robust indicators for overall survival (OS), and a prognostic model was built based on the IRGs signature. Meanwhile, patients with high risk had worse OS in training and validation cohort, and the area under the ROC was 0.898 and 0.846, respectively. Nomogram demonstrated that immune risk score contributed much more points than other clinicopathological variables, with a C-index of 0.819 (95% CI, 0.727-0.911). Finally, we found that IRGs signature was positively correlated with the proportion of CD8+ T cells, neurophils and T gamma delta, while negatively with that of CD4+ memory resting T cells.Conclusions: We established and validated an effective five IRGs-based prediction model for CCA, which could accurately classify patients into groups with low and high risk of poor prognosis.

scholarly journals Combining Genetic Mutation and Expression Profiles Identifies Novel Prognostic Biomarkers of Lung Adenocarcinoma

2020 ◽  
Vol 14 ◽  
pp. 117955492096626
Author(s):  
Yun Liu ◽  
Fu Liu ◽  
Xintong Hu ◽  
Jiaxue He ◽  
Yanfang Jiang
Keyword(s):  
High Risk ◽  
Lung Adenocarcinoma ◽  
Expression Profiles ◽  
Genetic Mutation ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Support Vector ◽  
Omics Data ◽  
Differential Analysis ◽  
Data Set

Motivation: Although several prognostic signatures for lung adenocarcinoma (LUAD) have been developed, they are mainly based on a single-omics data set. This article aims to develop a novel set of prognostic signatures by combining genetic mutation and expression profiles of LUAD patients. Methods: The genetic mutation and expression profiles, together with the clinical profiles of a cohort of LUAD patients from The Cancer Genome Atlas (TCGA), were downloaded. Patients were separated into 2 groups, namely, the high-risk and low-risk groups, according to their overall survivals. Then, differential analysis was performed to determine differentially expressed genes (DEGs) and mutated genes (DMGs) in the expression and mutation profiles, respectively, between the 2 groups. Finally, a prognostic model based on the support vector machine (SVM) algorithm was developed by combining the expression values of the DEGs and the mutation times of the DMGs. Results: A total of 13 DEGs and 7 DMGs were recognized between the 2 groups. Their prognostic values were validated using independent cohorts. Compared with several existing signatures, the proposed prognostic signatures exhibited better prediction performance in the testing set. In addition, it is found that 1 of the 7 DMGs, GRIN2B, is mutated much more frequently in the high-risk group, showing a potential value as a therapy target. Conclusions: Combining multi-omics data sets is an applicable manner to identify novel prognostic signatures and to improve the prognostic prediction for LUAD, which will be heuristic to other types of cancers.

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