Dataset Information

Identification and validation of a twelve immune infiltration-related lncRNA prognostic signature for bladder cancer.

ABSTRACT: The prognosis of bladder cancer patients is strongly related to both the immune-infiltrating cells and the expression of lncRNAs. In this study, we analyzed the infiltration of immune cells in 403 bladder cancer samples obtained from TCGA by applying the ssGSEA to these samples, then dividing them into high/low immune cell infiltration groups. Based on these groupings, we found 404 differentially expressed immune infiltration-related lncRNAs, which were successively analyzed by univariate Cox regression, then Least Absolute Shrinkage and Selection Operator (LASSO), and finally stepwise multiple Cox regression. Then 12 differentially expressed immune infiltration-related lncRNAs were identified and used to construct a prognostic signature for bladder cancer. Subsequently, Kaplan-Meier analysis, univariate Cox regression, multivariate Cox regression, and multivariate time-dependent ROC analyses (for 1, 3, 5 years) all revealed that this signature performed well in predicting overall survival and served as an independent prognostic factor for patients with bladder cancer. Finally, both TIMER and CIBESORT showed that this 12-lncRNA prognostic signature for bladder cancer was associated with the infiltration of immune cell subtypes. Besides, nomogram considered risk score and clinical characteristics was assembled and showed great performance. More importantly, we found our signature could well distinguish the drug response of patients with bladder cancer. High risk patients showed a better response to cisplatin, doxorubicin, and anti- CTLA4 immunotherapy, low risk patients showed a better response to methotrexate and anti-PD1 immunotherapy compared with each other.

SUBMITTER: Liu CQ

PROVIDER: S-EPMC8876923 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

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Identification and validation of a twelve immune infiltration-related lncRNA prognostic signature for bladder cancer.

Liu Chen-Qian CQ Xia Qi-Dong QD Sun Jian-Xuan JX Xu Jin-Zhou JZ Lu Jun-Lin JL Liu Zheng Z Hu Jia J Wang Shao-Gang SG

Aging 20220214 3

The prognosis of bladder cancer patients is strongly related to both the immune-infiltrating cells and the expression of lncRNAs. In this study, we analyzed the infiltration of immune cells in 403 bladder cancer samples obtained from TCGA by applying the ssGSEA to these samples, then dividing them into high/low immune cell infiltration groups. Based on these groupings, we found 404 differentially expressed immune infiltration-related lncRNAs, which were successively analyzed by univariate Cox re ...[more]

PMID: 35165206

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Project description:BackgroundRecently, researches have implied that immune-related lncRNAs (IR-lncRNAs) have a vital role in tumor occurrence and development. However, the study in bladder cancer (Bca) is still unclear. New biomarkers and reliable prognostic models for Bca are still limited. Here, we investigated the potential application value of immune-related lncRNAs in the prognostic evaluation of Bca patients.MethodsWe obtained clinical information and corresponding sequencing data from the The Cancer Genome Atlas (TCGA) database, and the cohort of Bca patients was divided into training and validation cohorts (ratio 7:3) randomly. An immune-related lncRNA co-expression network was constructed to identify immune-related lncRNAs. The candidate module intimately associated with overall survival (OS) was identified by using weighted gene co-expression network analysis (WGCNA). Univariate, multivariate Cox regression and LASSO analysis were performed to build the immune-related lncRNA signature. A prognostic model was further developed and its prognostic value was evaluated by Kaplan-Meier (KM) analysis. GSEA, KEGG analysis and GO annotation were used for functional annotation in this study.ResultsTotally, we identified 1,249 differentially expressed IR-lncRNAs were and six of which (AC005674.2, AC090948.1, TFAP2A-AS1, AL354919.2, AC011468.1 and AC018809.2) were finally selected in the gene signature. According to survival analysis, patients with high-risk scores were significantly related to poor survival outcomes. Furthermore, we established a novel gene signature demonstrated high prognostic value, and can be utilized as an independent risk factor (validation cohort: P<0.001, HR =3.832; training cohort: P<0.001, HR =2.843). Additionally, we built a nomogram on account of the clinicopathologic characteristics and gene signature to predict the survival probability of 1-, 3- and 5-year in Bca patients. The value of AUC curve of 1-, 3- and 5-year survival probability was 0.724, 0.777 and 0.77, severally. Furthermore, some enrichment pathways were identified by KEGG and GO analysis, and might be useful to display the potential mechanism for Bca.ConclusionsOur results indicated that the signature of six immune-related lncRNAs had an underlying value in the prognosis of Bca patients and might be helpful for the immunotherapy of Bca.

Project description:Increasing evidence has demonstrated that pyroptosis, a type of inflammatory programmed cell death, plays an important role in the pathogenesis and progression of gastric cancer. However, it remains unclear whether pyroptosis-related long non-coding RNAs (lncRNAs) can be used to predict the diagnosis and prognosis of gastric adenocarcinoma. This study aimed to evaluate and test the role of the lncRNA signature associated with pyroptosis as a prognostic tool for stomach adenocarcinoma (STAD) and to ascertain their immune value. Relative RNA-sequencing data were extracted from The Cancer Genome Atlas database (TCGA), and data preprocessing was performed for STAD. Pearson correlation analysis was used to determine whether lncRNAs were significantly correlated with pyroptosis based on 23 genes related to pyroptosis. Univariate Cox regression and least absolute shrinkage and selection operator(LASSO) analyses were both adopted to select features and establish the pyroptosis-related lncRNA (PRL) prognostic signature. Kaplan-Meier(KM) survival analysis of the different risk groups was conducted according to the risk scores. We further examined the functional enrichment, tumor microenvironment, and landscape of mutation status among the different risk groups, and these analyses further explained the reasons for the differences in the prediction as well as survival value of the different risk groups. Four lncRNAs, including HAND2-AS1, LINC01354, RP11-276H19.1, and PGM5-AS1, were involved in the PRL signature and used to split STAD patients into two risk groups. Overall survival time(OS) was significantly higher in the low-risk group than in the high-risk group in both the training and validation groups. Functional enrichment analysis was further employed to analyze differentially expressed genes in high- and low-risk groups to identify potential molecular functions and pathways associated with pyroptosis in the gastric cancer microenvironment. Protein-protein interaction (PPI) and Friends analysis identified hub genes that may play a key role in differentially expressed genes in high- and low-risk groups. In addition, there were remarkable discrepancies between the different risk groups in the tumor stage (P < 0.01) and histologic grade (P < 0.05). Furthermore, drug-susceptibility testing indicated potential sensitive chemotherapeutic drugs for each risk group. This study is the first to establish and validate STAD-associated PRLs that can effectively guide the prognosis and the immune microenvironment in STAD patients and provide evidence for the development of molecularly targeted therapies related to pyroptosis.

Project description:Background: This study aimed to explore the biological functions and prognostic role of Epithelial-mesenchymal transition (Epithelial-mesenchymal transition)-related lncRNAs in colorectal cancer (CRC). Methods: The Cancer Genome Atlas database was applied to retrieve gene expression data and clinical information. An EMT-related lncRNA risk signature was constructed relying on univariate Cox regression, Least Absolute Shrinkage and Selector Operation (LASSO) and multivariate Cox regression analysis of the EMT-related lncRNA expression data and clinical information. Then, an individualized prognostic prediction model based on the nomogram was developed and the predictive accuracy and discriminative ability of the nomogram were determined by the receiver operating characteristic curve and calibration curve. Finally, a series of analyses, such as functional analysis and unsupervised cluster analysis, were conducted to explore the influence of independent lncRNAs on CRC. Results: A total of 581 patients were enrolled and an eleven-EMT-related lncRNA risk signature was identified relying on the comprehensive analysis of the EMT-related lncRNA expression data and clinical information in the training cohort. Then, risk scores were calculated to divide patients into high and low-risk groups, and the Kaplan-Meier curve analysis showed that low-risk patients tended to have better overall survival (OS). Multivariate Cox regression analysis indicated that the EMT-related lncRNA signature was significantly associated with prognosis. The results were subsequently confirmed in the validation dataset. Then, we constructed and validated a predictive nomogram for overall survival based on the clinical factors and risk signature. Functional characterization confirmed this signature could predict immune-related phenotype and was associated with immune cell infiltration (i.e., macrophages M0, M1, Tregs, CD4 memory resting cells, and neutrophils), tumor mutation burden (TMB). Conclusions: Our study highlighted the value of the 11-EMT-lncRNA signature as a predictor of prognosis and immunotherapeutic response in CRC.

Dataset Information

Identification and validation of a twelve immune infiltration-related lncRNA prognostic signature for bladder cancer.

Publications

Identification and validation of a twelve immune infiltration-related lncRNA prognostic signature for bladder cancer.

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