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Identification of pyroptosis-related long non-coding RNAs with prognosis and therapy in lung squamous cell carcinoma.

ABSTRACT: Pyroptosis is a type of programmed cell death with an intense inflammatory response. Previous studies have shown that pyroptosis plays an important role in the pathogenesis and progression of lung cancer. However, the prognostic value and drug targets of pyroptosis-related lncRNAs in lung squamous cell carcinoma (LSCC) have never been studied. In the present study, we identified 1468 pyroptosis-related lncRNAs in LSCC by performing Pearson correlation analysis between the pyroptosis-related genes and the lncRNAs from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. The whole set was divided into a training and a test set with a 1:1 ratio. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses were conducted to establish an 11 multilncRNA signature in the three sets. The signature divided LSCC patients into the low-risk and the high-risk groups. Kaplan-Meier analysis and receiver operating characteristic (ROC) indicated that the prognostic signature had a promising predictive capability for LSCC patients. Besides, the association of microenvironment and immunotherapy response with signature was also analyzed. Moreover, 28 potential compounds targeting signature were screened as possible drugs to treat LSCC. Finally, a nomogram model was constructed to offer the quantitative prediction and net benefit for the prognosis of LSCC patients. In conclusion, the 11 pyroptosis-related lncRNAs and their signature may be promising prognostic factors and therapeutic targets for patients with LSCC.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC9249737 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background and objectiveOral squamous cell carcinoma (OSCC) is the most common malignant tumor in the head and neck, and its morbidity and mortality are increasing year by year. Changes in key genes are thought to be closely related to the occurrence and development of OSCC. Pyroptosis is an inflammatory form of programmed cell death that has been implicated in malignancies and inflammatory diseases. Changes in the expression of long noncoding RNAs may also affect tumorigenesis and progression. In this study, our main objective was to evaluate the association between pyroptosis-related lncRNAs and prognosis in patients with OSCC.MethodsThe RNA-seq data and clinicopathological data of OSCC patients are from The Cancer Genome Atlas database. The pyroptosis gene set is obtained from Gene Set Enrichment Analysis database. Univariate COX, Lasso and multivariate COX regression analyses were used for the construction of risk prognostic models of OSCC, eight lncRNAs were incorporated into prognostic models. The Kaplan-Meier method and log-rank test were used to evaluate the differences of overall survival between patients in high-risk and low-risk groups. The reliability of predictions across the dataset was analyzed by receiver operating characteristic (ROC) curves. The immune signature score was calculated using the single-sample gene set enrichment analysis.ResultsEight pyroptosis-related lncRNAs were used to construct prognostic signature of OSCC, including AC136475.2, AC024075.2, JPX, ZFAS1, TNFRSF10A-AS1, LINC00847, AC099850.3 and IER3-AS1. According to this prognostic signature, patients with OSCC were divided into high-risk and low-risk groups. Kaplan-Meier survival analysis showed that the survival rate of the high-risk group was significantly lower than the low-risk group. ROC area for risk score was 0.716, and ROC area of the 8 lncRNAs are all between 0.5 and 1, implied that these lncRNAs had high accuracy in predicting the prognosis of OSCC patients. Immune Infiltration findings suggested that these lncRNAs affected immune responses in the microenvironment of OSCC.ConclusionThe prognostic signature based on pyroptosis-related lncRNAs potentially serves as an independent prognostic indicator for OSCC patients. And this signature facilitates research on targeted diagnosis and treatment of patients diagnosed with OSCC.

Project description:Oral squamous cell carcinoma (OSCC) is one of the most common malignancies worldwide, due to poor diagnosis and treatment. There is increasing evidence that demonstrates the involvement of long non-coding RNAs (lncRNAs) in carcinogenesis and cancer progression. Therefore, the aim of the present study was to explore potential lncRNA-associated features of patients with OSCC as a valuable and independent prognostic biomarker. A total of 268 lncRNA expression profiles and clinical patient information on OSCC were downloaded from The Cancer Genome Atlas database. The clinical information was exploited for prescreening, using Cox regression analysis, and differentially expressed lncRNAs (DElncRNAs) were identified using edgeR software. Using the 'caret' package, the datasets were categorized into test datasets and training datasets, respectively. Through bioinformatics, seven prognostic DElncRNAs were selected. Using the regression coefficients, a risk score based on the seven-DElncRNA signature was developed to assess the prognostic function of key DElncRNAs. According to the median risk score, patients were classified into high-risk and low-risk groups in the training and test datasets. Additionally, receiver operating characteristic (ROC) curve analysis was conducted to evaluate the sensitivity and specificity of the prognostic DElncRNAs, and the optimal cut-off point was obtained from ROC analysis. Based on the optimal cut-off point, the patients were also categorized into high-risk and low-risk groups. Notably, the optimal cut-off point was more sensitive than the median risk score, particularly in the test dataset. The Kaplan-Meier survival and log rank test analysis results indicated that the P-value, based on the optimal cut-off, was less than the median risk cut-off. Additionally, stratified analysis results revealed that the seven-DElncRNAs signature was also independent of OSCC age. Furthermore, the findings of the present study suggested that the seven-DElncRNA signature can be used as a potential prognostic indicator and may have important clinical significance in OSCC.

Project description:BackgroundLong intergenic noncoding RNAs (lincRNAs) are a series of novel transcribed regions expressed in cancers that may represent candidate biomarkers for lung squamous cell carcinoma (LSqCC) treatment. In this study, we evaluated the lincRNA profile in LSqCC patients and screened valuable lincRNAs for diagnosis and prognosis.MethodsTranscriptome profiling of 549 samples derived from 501 LSqCC patients were identified in TCGA database. 48 patients had paired primary tumor (PT) and solid normal (SN) tissue samples, while 453 patients had only PT samples. 1,771 lincRNA candidates were evaluated. Paired test (Wilcoxon two-sample paired signed rank tests) was performed in paired PT and SN samples. Logistic regression analysis were performed in independent 453 PT samples and 48 SN samples to screen the significant lincRNAs candidates for malignances. Independent 501 PT samples were further used to screen the significant lincRNAs candidates for prognosis.ResultsAmong 1,771 lincRNAs, 10 lincRNAs were significant highly-expressed risk candidates in PT samples, and 10 protective lincRNAs candidates were significant lowly-expressed in PT samples. Among 10 highly-expressed risk lincRNAs, a small panel of LINC00487, LINC01927, and C10orf143 (LINC00959) could effectively predict malignancies in paired samples (AUC = 0.7274, 95%CI = (0.6264, 0.8285)). When combined with protective lincRNA candidates LINC02315, LINC00491, and LINC01697, the predictive efficiency was greatly improved in both paired samples (AUC = 0.8030, 95%CI = (0.7250, 0.8810)) and independent samples (AUC = 0.7481, 95%CI= (0.6642, 0.8320)). Additionally, three highly-expressed risk lincRNAs, LINC01031, LINC01088, and LINC01931, were significantly associated with poor prognosis in PT samples, suggesting potential targets for anti-LSqCC treatment.ConclusionTherefore, lincRNAs could be promising biomarkers for predicting malignancies and potential anti-LSqCC targets for drug development.

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Identification of pyroptosis-related long non-coding RNAs with prognosis and therapy in lung squamous cell carcinoma.

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