A robust signature of immune‐related long non‐coding RNA to predict the prognosis of bladder cancer

Lai, Cong; Wu, Zhenyu; Li, Zhuohang; Yu, Hao; Li, Kuiqing; Tang, Zhuang; Liu, Cheng; Xu, Kecheng

doi:10.1002/cam4.4167

Cited by 10 publications

(5 citation statements)

References 39 publications

(43 reference statements)

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“…For instance, lncRNA-ROR, a stress-responsive lncRNA highly expressed in HCC cells and enriched in extracellular vesicles derived from tumor cells, could induce primary chemoresistance by activating the TGF-β pathway in HCC (Takahashi et al, 2014). Moreover, immune-related lncRNA signatures have been reported as prognostic biomarkers and therapeutic targets in several tumors, including lung adenocarcinoma, bladder cancer, esophageal cancer, gastric cancer, rectal cancer, cervical cancer, and HCC (Chen et al, 2021;Lai et al, 2021;Liu et al, 2021;Nie et al, 2021;Pang et al, 2021;Song et al, 2021;Xing et al, 2021;Ye et al, 2021), indicating that immune-related lncRNAs could effectively be applied to determine the survival, prognosis, and treatment response. However, all these studies focused on excerpting the immune-related lncRNA profiles from the obtained immune-related genes based on co-expression analysis while ignoring the important role of the stromal components in tumor progression; immune-related genes remain inconclusive in different research studies, and there are no uniform criteria for filtering immune-related lncRNAs, which may limit the generalization of the predictive model.…”

Section: Discussionmentioning

confidence: 99%

Identification of Novel Tumor Microenvironment-Related Long Noncoding RNAs to Determine the Prognosis and Response to Immunotherapy of Hepatocellular Carcinoma Patients

Huang

Zhang

Lai

et al. 2021

Front. Mol. Biosci.

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Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. The tumor microenvironment (TME) plays a vital role in HCC progression. Thus, this research was designed to analyze the correlation between the TME and the prognosis of HCC patients and to construct a TME-related long noncoding RNA (lncRNA) signature to determine HCC patients’ prognosis and response to immunotherapy.Methods: We assessed the stromal–immune–estimate scores within the HCC microenvironment using the ESTIMATE (Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data) algorithm based on The Cancer Genome Atlas database, and their associations with survival and clinicopathological parameters were also analyzed. Thereafter, differentially expressed lncRNAs were filtered out according to the immune and stromal scores. Cox regression analysis was performed to build a TME-related lncRNA risk signature. Kaplan–Meier analysis was used to explore the prognostic value of the risk signature. Furthermore, we explored the biological functions and immune microenvironment features in the high- and low-risk groups. Lastly, we probed the association of the risk model with treatment responses to immune checkpoint inhibitors (ICIs) in HCC.Results: The stromal, immune, and estimate scores were obtained utilizing the ESTIMATE algorithm for patients with HCC. Kaplan–Meier analysis showed that high scores were significantly correlated with better prognosis in HCC patients. Six TME-related lncRNAs were screened to construct the prognostic model. The Kaplan–Meier curves suggested that HCC patients with low risk had better prognosis than those with high risk. Receiver operating characteristic (ROC) curve and Cox regression analyses indicated that the risk model could predict HCC survival exactly and independently. Functional enrichment analysis revealed that some tumor- and immune-related pathways were activated in the high-risk group. We also revealed that some immune cells, which were important in enhancing immune responses toward cancer, were significantly increased in the low-risk group. In addition, there was a close correlation between ICIs and the risk signature, which can be used to predict the treatment responses of HCC patients.Conclusion: We analyzed the influence of the stromal, immune, and estimate scores on the prognosis of HCC patients. A novel TME-related lncRNA risk model was established, which could be effectively applied as an independent prognostic biomarker and predictor of ICIs for HCC patients.

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Section: Discussionmentioning

confidence: 99%

Identification of Novel Tumor Microenvironment-Related Long Noncoding RNAs to Determine the Prognosis and Response to Immunotherapy of Hepatocellular Carcinoma Patients

Huang

Zhang

Lai

et al. 2021

Front. Mol. Biosci.

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“…Several prognostic signatures for bladder cancer were identified in previous studies. Regarding the prognosis prediction for bladder cancer patients, Lai et al [25] constructed and validated a predictive signature that included 9 immune-related lncRNAs. It was also found that autophagy-related, [26] EMT-related, [27] and N6-methyladenosine-related [28] lncRNA signatures were strongly associated with the prognosis.…”

Section: Discussionmentioning

confidence: 99%

Identification and validation of the pyroptosis-related long noncoding rna signature to predict the prognosis of patients with bladder cancer

Zeng

et al. 2023

Medicine

Self Cite

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Bladder cancer ranked the second most frequent tumor among urological malignancies. This work investigated bladder cancer prognosis, including the relevance of pyroptosis-related long noncoding RNA (lncRNA) in it and its potential roles. The Cancer Genome Atlas database offered statistics on lncRNAs and clinical data from 411 bladder cancer patients. Pearson correlation analysis was used to evaluate pyroptosis-related lncRNAs. To explore prognosis-associated lncRNAs, we performed univariate Cox regression, least absolute shrinkage and selection operator regression analyses, as well as the Kaplan–Meier method. Multivariate Cox analysis was leveraged to establish the risk score model. Afterward, a nomogram was constructed according to the risk score and clinical variables. Finally, to investigate the potential functions of pyroptosis-related lncRNAs, gene set enrichment analysis was employed. Eleven pyroptosis-related lncRNAs were screened to be closely associated with patients prognosis. On this foundation, a risk score model was created to classify patients into high and low risk groups. The signature was shown to be an independent prognostic factor (P < .001) with an area under the curve of 0.730. Then a nomogram was established including risk scores and clinical characteristics. The nomogram prediction effect is excellent, with a concordance index of 0.86. The 11-lncRNAs signature was associated with the supervision of oxidative stress, epithelial-mesenchymal transition, cell adhesion, TGF-β, and Wingless and INT-1 signaling pathway, according to the gene set enrichment analysis. Our findings indicate that pyroptosis-related lncRNAs, which may affect tumor pathogenesis in many ways, might be exploited to assess the prognosis of bladder cancer patients.

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“…Perhaps the key set of genes predicted by hdWGCNA via singlecell RNA could be integrated into the set of genes obtained by lasso regression via bulk RNA to further improve the accuracy of the diagnostic model. [31][32][33] At the advent of precision medicine, the unbiased multi-lineage method allows the mining of additional drug targets, as well as provides important information for the development of new targeted anti-fibrosis drugs.…”

Section: Discussionmentioning

confidence: 99%

Unraveling the activation process and core driver genes of HSCs during cirrhosis by single-cell transcriptome

Li,

Wang,

et al. 2023

Exp Biol Med (Maywood)

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Worldwide, cirrhosis is a common cause of death, manifesting itself as fibrosis of the liver tissue. When the liver is damaged, the liver produces fibrotic, proliferative myofibroblasts, which are formed by the differentiation of activated hepatic stellate cells. There are no effective antifibrotic treatment options. To deeply explore the activation process of hepatic stellate cells (HSCs) and to discover better therapeutic target genes, single-cell RNA sequencing data on 13 non-cirrhotic liver tissue samples and 10 cirrhotic liver tissue samples were analyzed. We identified activated HSCs from the mesenchymal cell population with high expression of ACTA2. By pseudo-time analysis, we found that the key genes for the differentiation of HSCs into myofibroblasts were C3, CCDC80, COL1A1, COL3A1, DCN, FBLN1, IGFBP3, MXRA5, SERPINE1, and MYH11. Then, we found that the main regulators of HSCs from inactive to activated state were NTF3, NTRK3, NTRK2, JAG1, NOTCH3, ESAM, and CD46 by cell–cell communication analysis. In addition, we found that the top2 hub genes of activated HSCs were CRIP1 and ACTA2. The experimental results show that the top2 hub genes were significantly overexpressed in cirrhotic samples. Our work dissected key intercellular regulators and core driver genes during hepatic stellate cell activation during cirrhosis through single-cell transcriptome data analysis, providing a research strategy to discover rational therapeutic targets for cirrhosis and some important information for gene targeting therapy.

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A robust signature of immune‐related long non‐coding RNA to predict the prognosis of bladder cancer

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 10 publications

References 39 publications

Identification of Novel Tumor Microenvironment-Related Long Noncoding RNAs to Determine the Prognosis and Response to Immunotherapy of Hepatocellular Carcinoma Patients

Identification of Novel Tumor Microenvironment-Related Long Noncoding RNAs to Determine the Prognosis and Response to Immunotherapy of Hepatocellular Carcinoma Patients

Identification and validation of the pyroptosis-related long noncoding rna signature to predict the prognosis of patients with bladder cancer

Unraveling the activation process and core driver genes of HSCs during cirrhosis by single-cell transcriptome

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