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

Shen, Cheng; Chen, Zhan; Jiang, Jie; Zhang, Yong; Chen, Xinfeng; Xu, Wei; Peng, Rui; Zuo, Wenjing; Jiang, Qian; Fan, Yihui; Fang, Xingxing; Zheng, Bing

doi:10.1038/s41598-023-34027-9

Cited by 3 publications

(3 citation statements)

References 59 publications

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“…Research has shown that SNHG17 is intimately connected to the growth, invasion, migration, resistance to chemicals, and apoptosis of tumor cells, and head and neck squamous cell carcinoma patients whose SNHG17 expression was high had a significantly unfavorable prognosis [17]. Studies showed that lncRNA AC103706.1 is associated with fat metabolism, copper apoptosis, and prognosis in ccRCC [51,52]. However, the relationship between six SRlncRNAs and CSCs has not been reported yet.…”

Section: Discussionmentioning

confidence: 99%

Stemness related lncRNAs signature for the prognosis and tumor immune microenvironment of ccRCC patients

Zhang,

Liang

et al. 2024

BMC Med Genomics

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Long non-coding RNAs (lncRNAs) and cancer stem cells (CSCs) are crucial for the growth, migration, recurrence, and medication resistance of tumors. However, the impact of lncRNAs related to stemness on the outcome and tumor immune microenvironment (TIME) in clear cell renal cell carcinoma (ccRCC) is still unclear. In this study, we aimed to predict the outcome and TIME of ccRCC by constructing a stem related lncRNAs (SRlncRNAs) signature. We firstly downloaded ccRCC patients’ clinical data and RNA sequencing data from UCSC and TCGA databases, and abtained the differentially expressed lncRNAs highly correlated with stem index in ccRCC through gene expression differential analysis and Pearson correlation analysis. Then, we selected suitable SRlncRNAs for constructing a prognostic signature of ccRCC patients by LASSO Cox regression. Further, we used nomogram and Kaplan Meier curves to evaluate the SRlncRNA signature for the prognose in ccRCC. At last, we used ssGSEA and GSVA to evaluate the correlation between the SRlncRNAs signature and TIME in ccRCC. Finally, We obtained a signtaure based on six SRlncRNAs, which are correlated with TIME and can effectively predict the ccRCC patients’ prognosis. The SRlncRNAs signature may be a noval prognostic indicator in ccRCC.

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Section: Discussionmentioning

confidence: 99%

Stemness related lncRNAs signature for the prognosis and tumor immune microenvironment of ccRCC patients

Zhang,

Liang

et al. 2024

BMC Med Genomics

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“…If Log2 FC (fold change) is greater than 1 or less than −1 (|log2 FC| > 1), we think that the gene is expressed significantly different in two risk groups. Furthermore, the false discovery rate (FDR) should be less than 0.05 [24,25]. Then, based on the Gene Ontology (GO) enrichment analysis, the Cellular Components (CC), Molecular Functions (MF) and Biological Processes (BP) between high-risk and low-risk groups were performed by the "clusterProfiler" package.…”

Section: Functional Enrichment Analysismentioning

confidence: 99%

Comprehensive Analysis of Disulfidptosis-Related LncRNAs in Molecular Classification, Immune Microenvironment Characterization and Prognosis of Gastric Cancer

Kang,

Li,

Peng

et al. 2023

Biomedicines

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Background: Disulfidptosis is a novel form of programmed cell death that unveils promising avenues for the exploration of tumor treatment modalities. Gastric cancer (GC) is a malignant tumor characterized by high incidence and mortality rate. However, there has been no systematic study of disulfidptosis-related long noncoding RNAs (DRLs) signature in GC patients. Methods: The lncRNA expression profiles containing 412 GC samples were acquired from the Cancer Genome Atlas (TCGA) database. Differential expression analysis was performed alongside Pearson correlation analysis to identify DRLs. Prognostically significant DRLs were further screened using univariate COX regression analysis. Subsequently, LASSO regression and multifactorial COX regression analyses were employed to establish a risk signature composed of DRLs that exhibit independent prognostic significance. The predictive value of this risk signature was further validated in a test cohort. The ESTIMATE, CIBERSORT and ssGSEA methodologies were utilized to investigate the tumor immune microenvironment of GC populations with different DRLs profiles. Finally, the correlation between DRLs and various GC drug responses was explored. Results: We established a prognostic signature comprising 12 disulfidptosis-related lncRNAs (AC110491.1, AL355574.1, RHPN1-AS1, AOAH-IT1, AP001065.3, MEF2C-AS1, AC016394.2, LINC00705, LINC01952, PART1, TNFRSF10A-AS1, LINC01537). The Kaplan–Meier survival analysis revealed that patients in the high-risk group exhibited a poor prognosis. Both univariate and multivariate COX regression models demonstrated that the DRLs signature was an independent prognostic indicator in GC patients. Furthermore, the signature exhibited accurate predictions of survival at 1-, 3- and 5- years with the area under the curve (AUC) values of 0.708, 0.689 and 0.854, respectively. In addition, we also observed significant associations between the DRLs signature and various clinical variables, distinct immune landscape and drug sensitivity profiles in GC patients. The low-risk group patients may be more likely to benefit from immunotherapy and chemotherapy. Conclusions: Our study investigated the role and potential clinical implications of DRLs in GC. The risk model constructed by DRLs demonstrated high accuracy in predicting the survival outcomes of GC and improving the treatment efficacy for GC patients.

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“…In another study, DRLs including ZEB1-AS1, SNHG16, and ALMS1-IT1 were found to be highly expressed in colon adenocarcinoma samples, predicting prognosis and in uencing the response to immunotherapy and chemotherapy in patients 34 . Additionally, the upregulation of FOXD2-AS1 and AC002070.1 in the immune microenvironment of kidney renal cells suggests their potential role in disul dptosis in kidney renal clear cell carcinoma 35 . The identi cation of DRLs holds fundamental implications for unraveling the speci c mechanisms underlying oncogenesis and predicting the prognosis of HNSC.…”

Section: Introductionmentioning

confidence: 99%

Identification of disulfidptosis-related lncRNA signature using RNA-Sequencing and Bioinformatics Analysis in Head and Neck Squamous Cell Carcinoma

Chen,

Shi,

Bao

et al. 2024

Preprint

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As a novel form of regulated cell death, disulfidptosis generating a favorable opportunity in better understanding of tumor pathogenesis and therapeutic strategies. Long non-coding RNAs (lncRNAs) regulate the biology functions of tumor cells with combination of variable targets. However, the prognostic value of disulfidptosis-related lncRNAs (DRlncRNAs) in head and neck squamous cell carcinoma (HNSC) remains largely unknown. Hence, we aimed to delve into the molecular characteristics of HNSC, focusing on disulfidptosis-related long non-coding RNAs (DRLs), and explore potential therapeutic strategies. We conducted lncRNA-mRNA RNA-Seq analyses on HNSC cell lines and utilized The Cancer Genome Atlas (TCGA) database, comprising RNA sequencing, clinical data, and gene mutation information from 522 HNSC tumors and 44 normal tissues. Bioinformatics analyses were employed to identify DRLs associated with disulfidptosis and assess their prognostic significance. Additionally, a risk model based on three selected DRLs was constructed to investigate its correlation with patient outcomes. Tumor mutation burden and chemotherapeutic responses were also explored in relation to the risk model. Our analysis pinpointed three DRLs (LINC02434, AC245041.2, and LINC02762) significantly correlated with HNSC prognosis. qRT-PCR results validated the consistency of LINC02434 and AC245041.2. The risk model, based on these DRLs, effectively stratified patients into high- and low-risk groups, revealing distinct survival patterns. Furthermore, the risk model demonstrated independent prognostic value in HNSC. Examination of the tumor microenvironment highlighted differences between high- and low-risk groups, suggesting potential implications for immunotherapeutic approaches. Specific chemotherapeutic agents with varying sensitivities across risk groups and molecular subtypes were identified. The identified three DRLs signature emerges as a novel biomarker with predictive value for HNSC prognosis. This study provides insights into potential therapeutic avenues and contributes to our understanding of the molecular landscape of HNSC.

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

Cited by 3 publications

References 59 publications

Stemness related lncRNAs signature for the prognosis and tumor immune microenvironment of ccRCC patients

Stemness related lncRNAs signature for the prognosis and tumor immune microenvironment of ccRCC patients

Comprehensive Analysis of Disulfidptosis-Related LncRNAs in Molecular Classification, Immune Microenvironment Characterization and Prognosis of Gastric Cancer

Identification of disulfidptosis-related lncRNA signature using RNA-Sequencing and Bioinformatics Analysis in Head and Neck Squamous Cell Carcinoma

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