scholarly journals Identification of a Four-lncRNA Prognostic Signature for Colon Cancer Based on Genome Instability

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Duo Yun ◽  
Zhirong Yang
Keyword(s):  
Colon Cancer ◽  
Somatic Mutation ◽  
Genome Instability ◽  
Noncoding Rnas ◽  
Cancer Biomarkers ◽  
Risk Groups ◽  
Mutation Rates ◽  
Risk Scores ◽  
Prognostic Signature ◽  
A Genome

LncRNAs (long noncoding RNAs) are closely associated with genome instability. However, the identification of lncRNAs related to the genome instability and their relationship with the prognosis and clinical signature of cancer remains to be explored. In this paper, we analyzed differential lncRNA expression based on the somatic mutation profiles of colon cancer patients from TCGA database and finally identified 153 lncRNAs that are associated with genome instability in colon cancer. Taking four lncRNAs from these 153, we established a genome-instability-related prognostic signature (GIRlncPSig). By applying the GIRlncPSig, we calculated a risk score for each patient, and using their risk scores, we divided them into low- and high-risk groups. We found that the prognosis between the two risk groups was significantly different, and the results were further verified in different independent patient cohorts. Moreover, we observed that the GIRlncPSig was related to somatic mutation rates in colon cancer, indicating that it may be a potential means of measuring genome instability levels in colon cancer. We also revealed that the GIRlncPSig was correlated with BRAF and DPYD mutation rates and that it may be a potential mutation marker for the BRAF and DPYD gene. In summary, we constructed a genome-instability-related lncRNA prognostic signature (GIRlncPSig), which has a significant effect on prognosis prediction and may allow for the discovery of new colon cancer biomarkers.

scholarly journals The lncRNA Signatures of Genome Instability to Predict Survival in Patients with Renal Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Liang Huang ◽  
Yu Xie ◽  
Shusuan Jiang ◽  
Weiqing Han ◽  
Fanchang Zeng ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Somatic Mutation ◽  
Renal Cancer ◽  
Somatic Mutations ◽  
Genome Instability ◽  
Noncoding Rnas ◽  
Cancer Biomarkers ◽  
Computational Framework ◽  
Mutation Profile ◽  
Prognosis Model

Long noncoding RNAs (lncRNAs) exert an increasingly important effect on genome instability and the prognosis of cancer patients. The present research established a computational framework originating from the mutation assumption combining lncRNA expression profile and somatic mutation profile in the genome of renal cancer to assess the effect of lncRNAs on the gene instability of renal cancer. A total of 45 differentially expressed lncRNAs were evaluated to be genome-instability-associated from the high and low cumulative somatic mutations groups. Then we established a prognosis model based on three genome-instability-associated lncRNAs (AC156455.1, AC016405.3, and LINC01234)-GlncScore. The GlncScore was then verified in testing cohort and the total TCGA renal cancer cohort. The GlncScore was evaluated to have an accurate prediction for the survival of patients. Furthermore, GlncScore was associated with somatic mutation patterns, indicating its capacity of reflecting genome instability in renal cancer. In conclusion, this study evaluated the effect of lncRNAs on genome instability of renal cancer and provided new hidden cancer biomarkers related to genome instability in renal cancer.

scholarly journals Identification of a Genome Instability-Associated LncRNA Signature for Prognosis Prediction in Colon Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Tengfei Yin ◽  
Dongyan Zhao ◽  
Shukun Yao
Keyword(s):  
Colon Cancer ◽  
Somatic Mutation ◽  
Survival Data ◽  
Prognostic Value ◽  
Genome Instability ◽  
Cancer Genome ◽  
Functional Enrichment ◽  
Prognosis Prediction ◽  
A Genome ◽  
Risk Patients

Long non-coding RNAs (lncRNAs) were reported to have the potential in maintaining genome instability, but the identification of lncRNAs related to genome instability and their prognostic value have not been largely explored in colon cancer. In this study, we obtained 155 genome instability-associated lncRNAs based on somatic mutation profiles in colon cancer from The Cancer Genome Atlas (TCGA) database. Functional enrichment analysis revealed the possible roles of genes co-expressed with those lncRNAs involved in some cancer, genome instability and immune related biological processes. Combined with overall survival data, a seven-lncRNA signature was established for prognosis prediction. According to the risk score calculated by this signature, high-risk patients characterized by high somatic mutation count, high microsatellite instability, significantly poorer clinical outcomes and specific tumor immune infiltration status compared with low-risk patients. The lncRNA signature was validated to be an independent prognostic indicator with good predictive performance in TCGA cohort. Furthermore, the prognostic value of the ZNF503-AS1 in lncRNA signature was confirmed in another independent dataset from Gene Expression Omnibus database. In summary, the genome instability-associated lncRNA signature in this study could be a promising tool for effectively predicting survival outcomes in colon cancer.

scholarly journals An immune-related lncRNA signature as a prognostic immunotherapy target for colon cancer

2020 ◽  
Author(s):  
Bin Wu ◽  
Yi Yao ◽  
Yi Dong ◽  
Si Qi Yang ◽  
Deng Jing Zhou ◽  
...  
Keyword(s):  
Colon Cancer ◽  
High Risk ◽  
Risk Score ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Risk Scores ◽  
Gene Set Enrichment ◽  
Immunotherapy Target

Abstract Background：We aimed to investigate an immune-related long non-coding RNA (lncRNA) signature that may be exploited as a potential immunotherapy target in colon cancer. Materials and methods: Colon cancer samples from The Cancer Genome Atlas (TCGA) containing available clinical information and complete genomic mRNA expression data were used in our study. We then constructed immune-related lncRNA co-expression networks to identify the most promising immune-related lncRNAs. According to the risk score developed from screened immune-related lncRNAs, the high-risk and low-risk groups were separated on the basis of the median risk score, which served as the cutoff value. An overall survival analysis was then performed to confirm that the risk score developed from screened immune-related lncRNAs could predict colon cancer prognosis. The prediction reliability was further evaluated in the independent prognostic analysis and receiver operating characteristic curve (ROC). A principal component analysis (PCA) and gene set enrichment analysis (GSEA) were performed for functional annotation. Results: Information for a total of 514 patients was included in our study. After multiplex analysis, 12 immune-related lncRNAs were confirmed as a signature to evaluate the risk scores for each patient with cancer. Patients in the low-risk group exhibited a longer overall survival (OS) than those in the high-risk group. Additionally, the risk scores were an independent factor, and the Area Under Curve (AUC) of ROC for accuracy prediction was 0.726. Moreover, the low-risk and high-risk groups displayed different immune statuses based on principal components and gene set enrichment analysis.Conclusions: Our study suggested that the signature consisting of 12 immune-related lncRNAs can provide an accessible approach to measuring the prognosis of colon cancer and may serve as a valuable antitumor immunotherapy.

Computational identification of mutator-derived lncRNA signatures of genome instability for improving the clinical outcome of cancers: a case study in breast cancer

2019 ◽  
Vol 21 (5) ◽  
pp. 1742-1755 ◽  
Author(s):  
Siqi Bao ◽  
Hengqiang Zhao ◽  
Jian Yuan ◽  
Dandan Fan ◽  
Zicheng Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Genomic Instability ◽  
Genome Instability ◽  
Risk Group ◽  
Expression Profiles ◽  
Risk Groups ◽  
Low Risk ◽  
A Genome ◽  
Significant Difference

Abstract Emerging evidence revealed the critical roles of long non-coding RNAs (lncRNAs) in maintaining genomic instability. However, identification of genome instability-associated lncRNAs and their clinical significance in cancers remain largely unexplored. Here, we developed a mutator hypothesis-derived computational frame combining lncRNA expression profiles and somatic mutation profiles in a tumor genome and identified 128 novel genomic instability-associated lncRNAs in breast cancer as a case study. We then identified a genome instability-derived two lncRNA-based gene signature (GILncSig) that stratified patients into high- and low-risk groups with significantly different outcome and was further validated in multiple independent patient cohorts. Furthermore, the GILncSig correlated with genomic mutation rate in both ovarian cancer and breast cancer, indicating its potential as a measurement of the degree of genome instability. The GILncSig was able to divide TP53 wide-type patients into two risk groups, with the low-risk group showing significantly improved outcome and the high-risk group showing no significant difference compared with those with TP53 mutation. In summary, this study provided a critical approach and resource for further studies examining the role of lncRNAs in genome instability and introduced a potential new avenue for identifying genomic instability-associated cancer biomarkers.

scholarly journals Identification of a Novel Prognostic Signature of Genome Instability-Related LncRNAs in Early Stage Lung Adenocarcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Bo Peng ◽  
Huawei Li ◽  
Ruisi Na ◽  
Tong Lu ◽  
Yongchao Li ◽  
...  
Keyword(s):  
High Risk ◽  
Lung Adenocarcinoma ◽  
Genomic Instability ◽  
Cox Regression ◽  
Genome Instability ◽  
Early Stage ◽  
Prognostic Signature ◽  
Training Set ◽  
A Genome ◽  
Testing Set

BackgroundIncreasing evidence has demonstrated that long non-coding RNAs (lncRNAs) play a crucial part in maintaining genomic instability. We therefore identified genome instability-related lncRNAs and constructed a prediction signature for early stage lung adenocarcinoma (LUAD) as well in order for classification of high-risk group of patients and improvement of individualized therapies.MethodsEarly stage LUAD RNA-seq and clinical data from The Cancer Genome Atlas (TCGA) were randomly divided into training set (n = 177) and testing set (n = 176). A total of 146 genomic instability-associated lncRNAs were identified based on somatic mutation profiles combining lncRNA expression profiles from TCGA by the “limma R” package. We performed Cox regression analysis to develop this predictive indicator. We validated the prognostic signature by an external independent LUAD cohort with microarray platform acquired from the Gene Expression Omnibus (GEO).ResultsA genome instability-related six-lncRNA-based gene signature (GILncSig) was established to divide subjects into high-risk and low-risk groups with different outcomes at statistically significant levels. According to the multivariate Cox regression and stratification analysis, the GILncSig was an independent predictive factor. Furthermore, the six-lncRNA signature achieved AUC values of 0.745, 0.659, and 0.708 in the training set, testing set, and TCGA set, respectively. When compared with other prognostic lncRNA signatures, the GILncSig also exhibited better prediction performance.ConclusionThe prognostic lncRNA signature is a potent tool for risk stratification of early stage LUAD patients. Our study also provided new insights for identifying genome instability-related cancer biomarkers.

scholarly journals Comprehensive Analysis of the Prognostic Significance of Hsa-miR-100-5p and Its Related Gene Signature in Stomach Adenocarcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Gaoming Wang ◽  
Ludi Yang ◽  
Miao Hu ◽  
Renhao Hu ◽  
Yongkun Wang ◽  
...  
Keyword(s):  
High Risk ◽  
Cox Regression ◽  
Prognostic Significance ◽  
Tumor Development ◽  
Risk Groups ◽  
Gene Signature ◽  
Risk Scores ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Stomach Adenocarcinoma

Stomach adenocarcinoma (STAD) is one of the most common cancers in the world. However, the prognosis of STAD remains poor, and the therapeutic effect of chemotherapy and immunotherapy varies from person to person. MicroRNAs (miRNAs) play vital roles in tumor development and metastasis and can be used for cancer diagnosis and prognosis. In this study, hsa-miR-100-5p was identified as the only dysregulated miRNA in STAD samples through an analysis of three miRNA expression matrices. A weighted gene co-expression network analysis (WGCNA) was performed to select hsa-miR-100-5p-related genes. A least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to establish a miR-100-5p-related prognostic signature. Kaplan–Meier analyses, nomograms, and univariate and multivariate Cox regression analyses were used to evaluate the prognostic signature, which was subsequently identified as an independent risk factor for STAD patients. We investigated the tumor immune environment between low- and high-risk groups and found that, among component types, M2 macrophages contributed the most to the difference between these groups. A drug sensitivity analysis suggested that patients with high-risk scores may be more sensitive to docetaxel and cisplatin chemotherapy and that patients in the low-risk group may be more likely to benefit from immunotherapy. Finally, external cohorts were evaluated to validate the robustness of the prognostic signature. In summary, this study may provide new ideas for developing more individualized therapeutic strategies for STAD patients.

scholarly journals Identification of the Prognostic Significance of Somatic Mutation-Derived LncRNA Signatures of Genomic Instability in Lung Adenocarcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Wei Geng ◽  
Zhilei Lv ◽  
Jinshuo Fan ◽  
Juanjuan Xu ◽  
Kaimin Mao ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Genomic Instability ◽  
Somatic Mutation ◽  
Cox Regression ◽  
Genome Instability ◽  
External Validation ◽  
Prognostic Significance ◽  
Tumor Stage ◽  
Lasso Regression ◽  
A Genome

Background: Lung adenocarcinoma (LUAD) is a highly heterogeneous tumor with substantial somatic mutations and genome instability, which are emerging hallmarks of cancer. Long non-coding RNAs (lncRNAs) are promising cancer biomarkers that are reportedly involved in genomic instability. However, the identification of genome instability-related lncRNAs (GInLncRNAs) and their clinical significance has not been investigated in LUAD.Methods: We determined GInLncRNAs by combining somatic mutation and transcriptome data of 457 patients with LUAD and probed their potential function using co-expression network and Gene Ontology (GO) enrichment analyses. We then filtered GInLncRNAs by Cox regression and LASSO regression to construct a genome instability-related lncRNA signature (GInLncSig). We subsequently evaluated GInLncSig using correlation analyses with mutations, external validation, model comparisons, independent prognostic significance analyses, and clinical stratification analyses. Finally, we established a nomogram for prognosis prediction in patients with LUAD and validated it in the testing set and the entire TCGA dataset.Results: We identified 161 GInLncRNAs, of which seven were screened to develop a prognostic GInLncSig model (LINC01133, LINC01116, LINC01671, FAM83A-AS1, PLAC4, MIR223HG, and AL590226.1). GInLncSig independently predicted the overall survival of patients with LUAD and displayed an improved performance compared to other similar signatures. Furthermore, GInLncSig was related to somatic mutation patterns, suggesting its ability to reflect genome instability in LUAD. Finally, a nomogram comprising the GInLncSig and tumor stage exhibited improved robustness and clinical practicability for predicting patient prognosis.Conclusion: Our study identified a signature for prognostic prediction in LUAD comprising seven lncRNAs associated with genome instability, which may provide a useful indicator for clinical stratification management and treatment decisions for patients with LUAD.

scholarly journals Pyroptosis-associated molecular classification and prognostic signature in glioma

2021 ◽  
Author(s):  
Yanyan Li ◽  
Lin Shen ◽  
Na Li ◽  
Yajie Zhao ◽  
Qin Zhou ◽  
...  
Keyword(s):  
High Risk ◽  
Clinical Features ◽  
Immune Cell ◽  
Risk Groups ◽  
Molecular Classification ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Prognostic Signature ◽  
Molecular Alterations ◽  
Genome Atlas

Purpose: Integrative analysis was performed in the Chinese Glioma Genome Atlas and The Cancer Genome Atlas to describe the pyroptosis-associated molecular classification and prognostic signature in glioma. Experimental Design: Pyroptosis-related genes were used for consensus clustering and to develop a prognostic signature. The immune statuses, molecular alterations and clinical features of differentially expressed genes were analyzed among different subclasses and risk groups. A lncRNA-miRNA-mRNA network was built, and drug sensitivity analysis was used to identify small molecular drugs for the identified genes. Results: Glioma can be divided into two subclasses using 30 pyroptosis-related genes. Cluster 1 displayed high immune signatures and poor prognosis as well as high immune-related function scores. A prognostic signature based on 15 pyroptosis-related genes of the CGGA cohort can predict the overall survival of glioma and was well validated in the TCGA cohort. Cluster 1 had higher risk scores. The high-risk group had high immune cell and function scores and low DNA methylation of pyroptosis-related genes. The differences in pyroptosis-related gene mutations and somatic copy numbers were significant between the high-risk and low-risk groups. The ceRNA regulatory network uncovered the regulatory patterns of different risk groups in glioma. Nine pairs of target genes and drugs were identified. Conclusions: Pyroptosis-related genes can reflect the molecular biological and clinical features of glioma subclasses. The established prognostic signature can predict prognosis and distinguish molecular alterations in glioma patients. Our comprehensive analyses provide valuable guidelines for improving glioma patient management and individualized therapy.

scholarly journals Comprehensive Analysis of Pyroptosis-Associated in Molecular Classification, Immunity and Prognostic of Glioma

2022 ◽  
Vol 12 ◽  
Author(s):  
Peng Chen ◽  
Yanyan Li ◽  
Na Li ◽  
Liangfang Shen ◽  
Zhanzhan Li
Keyword(s):  
High Risk ◽  
Clinical Features ◽  
Immune Cell ◽  
Risk Groups ◽  
Molecular Classification ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Prognostic Signature ◽  
Molecular Alterations ◽  
Genome Atlas

Integrative analysis was performed in the Chinese Glioma Genome Atlas and The Cancer Genome Atlas to describe the pyroptosis-associated molecular classification and prognostic signature in glioma. Pyroptosis-related genes were used for consensus clustering and to develop a prognostic signature. The immune statuses, molecular alterations, and clinical features of differentially expressed genes were analyzed among different subclasses and risk groups. A lncRNA-miRNA-mRNA network was built, and drug sensitivity analysis was used to identify small molecular drugs for the identified genes. Glioma can be divided into two subclasses using 30 pyroptosis-related genes. Cluster 1 displayed high immune signatures and poor prognosis as well as high immune-related function scores. A prognostic signature based on 15 pyroptosis-related genes of the CGGA cohort can predict the overall survival of glioma and was well validated in the TCGA cohort. Cluster 1 had higher risk scores. The high-risk group had high immune cell and function scores and low DNA methylation of pyroptosis-related genes. The differences in pyroptosis-related gene mutations and somatic copy numbers were significant between the high-risk and low-risk groups. The ceRNA regulatory network uncovered the regulatory patterns of different risk groups in glioma. Nine pairs of target genes and drugs were identified. In vitro, CASP8 promotes the progression of glioma cells. Pyroptosis-related genes can reflect the molecular biological and clinical features of glioma subclasses. The established prognostic signature can predict prognosis and distinguish molecular alterations in glioma patients. Our comprehensive analyses provide valuable guidelines for improving glioma patient management and individualized therapy.

scholarly journals Identification of Molecular Subtypes and a Prognostic Signature Based on Inflammation-Related Genes in Colon Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Chenjie Qiu ◽  
Wenxiang Shi ◽  
Huili Wu ◽  
Shenshan Zou ◽  
Jianchao Li ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Cancer Patients ◽  
Immune Cell ◽  
Molecular Subtypes ◽  
Differential Expression Analysis ◽  
Human Leukocyte ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Immune Checkpoints ◽  
Prognostic Signature

Both tumour-infiltrating immune cells and inflammation-related genes that can mediate immune infiltration contribute to the initiation and prognosis of patients with colon cancer. In this study, we developed a method to predict the survival outcomes among colon cancer patients and direct immunotherapy and chemotherapy. We obtained patient data from The Cancer Genome Atlas (TCGA) and captured inflammation-related genes from the GeneCards database. The package “ConsensusClusterPlus” was used to generate molecular subtypes based on inflammation-related genes obtained by differential expression analysis and univariate Cox analysis. A prognostic signature including four genes (PLCG2, TIMP1, BDNF and IL13) was also constructed and was an independent prognostic factor. Cluster 2 and higher risk scores meant worse overall survival and higher expression of human leukocyte antigen and immune checkpoints. Immune cell infiltration calculated by the estimate, CIBERSORT, TIMER, ssGSEA algorithms, tumour immune dysfunction and exclusion (TIDE), and tumour stemness indices (TSIs) were also compared on the basis of inflammation-related molecular subtypes and the risk signature. In addition, analyses of stratification, somatic mutation, nomogram construction, chemotherapeutic response prediction and small-molecule drug prediction were performed based on the risk signature. We finally used qRT–PCR to detect the expression levels of four genes in colon cancer cell lines and obtained results consistent with the prediction. Our findings demonstrated a four-gene prognostic signature that could be useful for prognostication in colon cancer patients and designing personalized treatments, which could provide new versions of personalized management for these patients.

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