Dataset Information

A disulfidptosis-related lncRNA index predicting prognosis and the tumor microenvironment in colorectal cancer.

ABSTRACT: Colorectal cancer (CRC) is a common and deadly cancer worldwide with a high lethality rate. Disulfidptosis has been found to be an emerging mode of death in cancer, and the purpose of this study was to explore the relationship between disulfidptosis-related lncRNAs (DRLs) and CRC and to develop a prognostic model for CRC and DRLs. The gene expression data and clinicopathologic information of colorectal cancer patients were obtained from The Cancer Genome Atlas (TCGA) and screened for DRLs based on correlation analysis. The least absolute shrinkage and selection operator (LASSO) and Cox regression were used to construct the prognostic model, and its validation was carried out by PCA and receiver operating characteristic (ROC) curves. We constructed nomograms combined with the model. Finally, the possible mechanisms by which lncRNAs affect CRC were explored by functional enrichment analysis, immune infiltration and immune escape analysis. In summary, we developed a prognostic marker consisting of lncRNAs associated with disulfidptosis to help clinicians predict the survival of different CRC patients and use different targeted therapies and immunotherapies depending on the condition.

SUBMITTER: Xiao L

PROVIDER: S-EPMC10656577 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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A disulfidptosis-related lncRNA index predicting prognosis and the tumor microenvironment in colorectal cancer.

Xiao Lijun L Yin Wen W Chen Xuanqin X Zhang Xu X Zhang Chao C Yu Zehui Z Lü Muhan M

Scientific reports 20231116 1

Colorectal cancer (CRC) is a common and deadly cancer worldwide with a high lethality rate. Disulfidptosis has been found to be an emerging mode of death in cancer, and the purpose of this study was to explore the relationship between disulfidptosis-related lncRNAs (DRLs) and CRC and to develop a prognostic model for CRC and DRLs. The gene expression data and clinicopathologic information of colorectal cancer patients were obtained from The Cancer Genome Atlas (TCGA) and screened for DRLs based ...[more]

PMID: 37978247

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Project description:As a novel form of regulated cell death (RCD), disulfidptosis offering a significant opportunity in better understanding of tumor pathogenesis and therapeutic strategies. Long non-coding RNAs (lncRNAs) regulate the biology functions of tumor cells by engaging with a range of targets. However, the prognostic value of disulfidptosis-related lncRNAs (DRlncRNAs) in lung adenocarcinoma (LUAD) remains unclear. Therefore, our study aimed at establishing a prognostic model for LUAD patients based on DRlncRNAs. RNA-seq data and clinical information were obtained from The Cancer Genome Atlas (TCGA) database. Subsequently, a prognostic model based on DRlncRNAs was constructed using LASSO and COX regression analysis. Patients were stratified into high- and low-risk groups based on their risk scores. Differences between the high-risk and low-risk groups were investigated in terms of overall survival (OS), functional enrichment, tumor immune microenvironment (TIME), somatic mutations, and drug sensitivity. Finally, the role of lncRNA GSEC in LUAD was validated through in vitro experiments. Using the prognostic model consists of 5 DRlncRNAs (AL365181.2, GSEC, AC093673.1, AC012615.1, AL606834.1), the low-risk group exhibited a markedly superior survival in comparison to the high-risk group. The significant differences were observed among patients from different risk groups in OS, immune cell infiltration, immune checkpoint expression, immunotherapy response, and mutation landscape. Experimental results from cellular studies demonstrate the knockdown of lncRNA GSEC leading to a significant reduction in the proliferation and migration abilities of LUAD cells. Our prognostic model, constructed using 5 DRlncRNAs, exhibited the capacity to independently predict the survival of LUAD patients, providing the potentially significant assistance in prognosis prediction, and treatment effects optimization. Moreover, our study established a foundation for further research on disulfidptosis in LUAD and proposed new perspectives for the treatment of LUAD.

Project description:BackgroundDisulfidptosis is a newly discovered type of cell death that differs from apoptosis, necrosis, ferroptosis and other death modes and is closely related to the occurrence and progression of tumors. However, the predictive potential and biological characteristics of disulfidptosis-related lncRNAs (DRGs-lncRNAs) in esophageal squamous cell carcinoma (ESCC) are unclear.MethodsRNA transcriptome data, clinical information and mutation data for ESCC patients were obtained from The Cancer Genome Atlas (TCGA) database. Pearson correlation and Cox regression analyses were used to identify the DRGs-lncRNAs associated with overall survival (OS). LASSO regression analysis was used to construct the prognostic model. A nomogram was created to predict the prognosis of patients with ESCC. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were used to identify the signaling pathways associated with the model. TIMER, CIBERSORT, ESTIMATE and other methods were used to analyze immune infiltration, immune function, immune checkpoints and drug sensitivity. The tumor mutation burden (TMB) were assessed between different risk groups. Real-time polymerase chain reaction (RT‒PCR) was used to detect the expression of DRGs-lncRNAs in ESCC cell lines.ResultsA total of 155 lncRNAs significantly associated with disulfidptosis were identified. Through univariate Cox regression analysis, LASSO regression analysis and multivariate Cox regression analysis, 9 lncRNAs with independent prognostic significance were selected, and a prognosis model was established. Survival analysis with the prognostic model revealed that there were obvious differences in survival between the high- and low-risk groups. Further analysis revealed that the immune microenvironment, immune infiltration, immune function, immune checkpoints, and drug sensitivity significantly differed between the high-risk and low-risk groups. Patients who exhibited both high risk and high tumor mutation burden (TMB) survived shorter, while those who fell into the low risk and low TMB categories survived longer. In addition, RT‒PCR analysis revealed differential expression of DRG lncRNAs between ESCC cell lines and esophageal epithelial cell lines.ConclusionsWe established a DRG-lncRNA prognostic model that can be used to predict the prognosis, tumor mutation burden, immune cell infiltration, and drug sensitivity of ECSS patients. The results of this study provide valuable insights into the understanding of ESCC and provide valuable assistance for the individualized treatment of ESCC patients.

Dataset Information

A disulfidptosis-related lncRNA index predicting prognosis and the tumor microenvironment in colorectal cancer.

Publications

A disulfidptosis-related lncRNA index predicting prognosis and the tumor microenvironment in colorectal cancer.

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