Identification of a combined lncRNA prognostic signature and knockdown of lncRNA MANCR to inhibit progression of clear cell renal cell carcinoma by bioinformatics analysis

Xue, Song; Guo, Hanxu; Wei, Shibo; Yuan, Songliu; Huang, Jian; Deng, Shuo; Li, Qingwen; Li, Wenyong

doi:10.21037/tau-22-527

Cited by 3 publications

(3 citation statements)

References 36 publications

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“…The high-risk group also had greater type I IFN response scores and less antitumor immunity in addition to increased tumor immune cell infiltration. IFN production is a major regulator of PD-L1 expression in tumor cells and host cells, and it may enhance the benefit of anti-PD-1 therapy 50 . Therefore, a diminished antitumor immune response may portend worse outcome for those at risk.…”

Section: Discussionmentioning

confidence: 99%

Identification and validation of fatty acid metabolism-related lncRNA signatures as a novel prognostic model for clear cell renal cell carcinoma

Shen

Chen

Jiang

et al. 2023

Sci Rep

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Clear cell renal cell carcinoma (ccRCC) is a main subtype of renal cancer, and advanced ccRCC frequently has poor prognosis. Many studies have found that lipid metabolism influences tumor development and treatment. This study was to examine the prognostic and functional significance of genes associated with lipid metabolism in individuals with ccRCC. Using the database TCGA, differentially expressed genes (DEGs) associated with fatty acid metabolism (FAM) were identified. Prognostic risk score models for genes related to FAM were created using univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses. Our findings demonstrate that the prognosis of patients with ccRCC correlate highly with the profiles of FAM-related lncRNAs (AC009166.1, LINC00605, LINC01615, HOXA-AS2, AC103706.1, AC009686.2, AL590094.1, AC093278.2). The prognostic signature can serve as an independent predictive predictor for patients with ccRCC. The predictive signature's diagnostic effectiveness was superior to individual clinicopathological factors. Between the low- and high-risk groups, immunity research revealed a startling difference in terms of cells, function, and checkpoint scores. Chemotherapeutic medications such lapatinib, AZD8055, and WIKI4 had better outcomes for patients in the high-risk group. Overall, the predictive signature can help with clinical selection of immunotherapeutic regimens and chemotherapeutic drugs, improving prognosis prediction for ccRCC patients.

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Section: Discussionmentioning

confidence: 99%

Identification and validation of fatty acid metabolism-related lncRNA signatures as a novel prognostic model for clear cell renal cell carcinoma

Shen

Chen

Jiang

et al. 2023

Sci Rep

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“…The research methods mainly include the collection (collection and screening), processing (editing, sorting, management, and display), utilization (calculation and simulation), and analysis of biological data ( 9 , 10 ). Experience has shown that bioinformatics technology has great application value in the screening of disease biomarkers, which is of great significance for the diagnosis, treatment, and prognosis of diseases, and can provide a more comprehensive and profound understanding of diseases ( 11 - 14 ). A recent study showed that differentially expressed genes (DEGs) were significantly enriched in metabolic pathways, oxidative phosphorylation, and extracellular matrix (ECM) receptor interactions, and were closely related to fibrosis, collagen catabolic process and inflammatory response function ( 15 ).…”

Section: Introductionmentioning

confidence: 99%

Screening and identification of hub genes for ischemic cardiomyopathy and construction and validation of a clinical prognosis model using bioinformatics analysis

Wang,

Tang,

Bai

et al. 2024

J Thorac Dis

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Background Myocardial ischemia and hypoxia may result in myocardial cell necrosis, scar formation, and hyperplasia. We aim to explore the differentially expressed genes (DEGs) in ischemic cardiomyopathy (ICM), construct and identify a clinical prognosis model using bioinformatics methods, so as to screen potential biomarkers of ICM to provide a basis for the early diagnosis and treatment of ICM. Methods Based on the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database, R language was used to screen DEGs in healthy myocardial (n=5) and ICM myocardial tissues (n=12). DEGs were analyzed by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction (PPI). Receiver operating characteristic (ROC) curves were drawn to verify the target genes. Results A total of 259 genes with significantly changed fold change (FC) values were obtained through conditional screening, including up-regulated genes and down-regulated genes. The first two hub genes [interleukin-6 ( IL-6 ) and Ras homologous gene family member A ( RHOA )] with the largest degree value among the above up-regulated and down-regulated genes were selected and their expression values were combined in the gene chip to draw the ROC curve based on the pROC package of R language. The area under the ROC curve (AUC) values of IL-6 and RHOA were 0.956 and 0.995, respectively. The expression levels of Sqstm1, Nos2, IL-6, RHOA , and Zfp36 genes in the ICM group are lower than those in the blank control group and the difference was statistically significant (P<0.05). RHOA and Stat3 were identified as the key genes controlling the occurrence and development of ICM. Conclusions ICM is closely related to the changes of extracellular matrix (ECM) and oxidoreductase activity. The IL-6 and RHOA are expected to become potential targets for ICM treatment.

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“…A growing body of evidence indicates that lncRNA plays an important role in the development and prognosis of tumors ( 16 ). Xue et al found that the mitotically-associated lncRNA MANCR could play an important influence on the progression of ccRCC by constructing a prognostic lncRNA model ( 17 ). In addition, Zhang et al found that the cuproptosis-associated lncRNA signature could be used to predict the immune infiltration and prognosis of bladder cancer patients by systematically analyzing the expression characteristics of cuproptosis-associated lncRNA ( 18 ).…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a novel 5 cuproptosis-related long noncoding RNA signature to predict diagnosis, prognosis, and drug therapy in clear cell renal cell carcinoma

Chen¹,

Hu²,

Wei³

et al. 2023

Transl Androl Urol

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Background: Cuproptosis has been reported as a new form of cell death. However, its potential mechanism of action in clear cell renal cell carcinoma (ccRCC) remains unclear. Therefore, we systematically clarified the role of cuproptosis in ccRCC and aimed to develop a novel signature of cuproptosis-related long noncoding RNAs (lncRNA) (CRLs) to assess the clinical characteristics of ccRCC patients.Methods: Gene expression, copy number variation, gene mutation, and clinical data for ccRCC were obtained from The Cancer Genome Atlas (TCGA). CRL signature was constructed with least absolute shrinkage and selection operator (LASSO) regression analysis. The clinical diagnostic value of the signature was verified by clinical data. The prognostic value of the signature was detected by Kaplan-Meier analysis and receiver operating characteristic (ROC) curve. The prognostic value of the nomogram was evaluated by calibration curves, ROC curves, and decision curve analysis (DCA). Gene set enrichment analysis (GSEA), single sample GSEA (ssGSEA) and cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm were used to analyze the differences of immune function and immune cell infiltration among different risk groups. Prediction of clinical treatment differences in populations with different risks and susceptibilities was completed with R package (The R Foundation of Statistical Computing). Verification of key lncRNA expression was performed by quantitative real-time polymerase chain reaction (qRT-PCR).Results: The cuproptosis-related genes were extensively dysregulated in ccRCC. A total of 153 differentially expressed prognostic CRLs were identified in ccRCC. Furthermore, a 5-lncRNA signature (AC015912.3, AC026401.3, AC103706.1, AC134312.5, and EMX2OS) were obtained that showed good performance in the diagnosis and prognosis of ccRCC. The nomogram could more accurately predict overall survival (OS). Immune functions such as T-cell and B-cell receptor signaling pathways showed differences between different risk groups. Clinical treatment value analysis showed that the signature may be able to effectively guide immunotherapy and target therapy. In addition, qRT-PCR results showed significant differences in the expression of key lncRNAs in ccRCC.Conclusions: Cuproptosis plays an important role in the progression of ccRCC. The 5-CRL signature can guide the prediction of clinical characteristics and tumor immune microenvironment of ccRCC patients.

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Identification of a combined lncRNA prognostic signature and knockdown of lncRNA MANCR to inhibit progression of clear cell renal cell carcinoma by bioinformatics analysis

Cited by 3 publications

References 36 publications

Identification and validation of fatty acid metabolism-related lncRNA signatures as a novel prognostic model for clear cell renal cell carcinoma

Identification and validation of fatty acid metabolism-related lncRNA signatures as a novel prognostic model for clear cell renal cell carcinoma

Screening and identification of hub genes for ischemic cardiomyopathy and construction and validation of a clinical prognosis model using bioinformatics analysis

Development and validation of a novel 5 cuproptosis-related long noncoding RNA signature to predict diagnosis, prognosis, and drug therapy in clear cell renal cell carcinoma

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