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Novel prognostic signature for lung adenocarcinoma based on immune-related mRNA pairs.

ABSTRACT: Lung adenocarcinoma (LUAD) is a highly lethal malignant tumor. While the involvement of multiple mRNAs in the progression of LUAD is well established, the potential diagnostic value of immune-related mRNAs (irmRNAs) in LUAD remains largely unexplored. In this study, we utilized RNA-seq, clinical data, and immune-related gene information from LUAD patients to identify differentially expressed immune-related mRNAs (DEirmRNAs) and developed a predictive risk model based on specific DEirmRNA pairs closely linked with patient prognosis. We classified patients into high-risk and low-risk groups and analyzed factors such as survival rate, clinical characteristics, gene enrichment, immune cell infiltration, tumor mutation load, and drug susceptibility. We confirmed the expression levels of these DEirmRNAs in tumor tissues using qRT-PCR assay. Our results showed that the low-risk group had a longer survival time and lower tumor mutation burden (TMB) and microsatellite instability (MSI) compared to the high-risk group. The high-risk group also had a significant reduction in the number of certain immune cells and a lower half-maximum inhibitor concentration (IC50). We identified specific DEirmRNA pairs that were up-regulated or down-regulated in tumor tissues compared to adjacent tissues. Our prognostic risk model based on DEirmRNA pairs could be used to predict the prognosis of LUAD patients and provide reference for better treatment.

SUBMITTER: Yue J

PROVIDER: S-EPMC10839877 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Novel prognostic signature for lung adenocarcinoma based on immune-related mRNA pairs.

Yue Jiawei J Guo Hui H Ma Jinhong J Shi Weifeng W Wu Yumin Y

Heliyon 20240120 3

Lung adenocarcinoma (LUAD) is a highly lethal malignant tumor. While the involvement of multiple mRNAs in the progression of LUAD is well established, the potential diagnostic value of immune-related mRNAs (irmRNAs) in LUAD remains largely unexplored. In this study, we utilized RNA-seq, clinical data, and immune-related gene information from LUAD patients to identify differentially expressed immune-related mRNAs (DEirmRNAs) and developed a predictive risk model based on specific DEirmRNA pairs c ...[more]

PMID: 38317924

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Project description:BackgroundSuppressive tumor microenvironment is closely related to the progression and poor prognosis of lung adenocarcinoma (LUAD). Novel individual and universal immune-related biomarkers to predict the prognosis and immune landscape of LUAD patients are urgently needed. Two-gene pairing patterns could integrate and utilize various gene expression data.MethodsThe RNA-seq and relevant clinicopathological data of the LUAD project from the TCGA and well-known immune-related genes list from the ImmPort database were obtained. Co-expression analysis followed by an analysis of variance was performed to identify differentially expressed immune-related lncRNA (irlncRNA) (DEirlncRNA) between tumor and normal tissues. Two arbitrary DEirlncRNAs (DEirlncRNAs pair) in a tumor sample underwent pairwise comparison to generate a score (0 or 1). Next, Univariate analysis, Lasso regression and Multivariate analysis were used to screen survival-related DEirlncRNAs pairs and construct a prognostic model. The Acak information standard (AIC) values of the receiver operating characteristic (ROC) curve for 3 years are calculated to determine the cut-off point for high- or low-risk score. Finally, we evaluated the relationship between the risk score and overall survival, clinicopathological features, immune landscape, and chemotherapy efficacy.ResultsData of 54 normal and 497 tumor samples of LUAD were enrolled. After a strict screening process, 15 survival-independent-related DEirlncRNA pairs were integrated to construct a prognostic model. The AUC value of the 3-year ROC curve was 0.828. Kaplan-Meier analysis showed that patients with low risk lived longer than patients with high risk (p <0.001). Univariate and Multivariate Cox analysis suggested that the risk score was an independent factor of survival. The risk score was negatively associated with most tumor-infiltrating immune cells, immune score, and microenvironment scores. The low-risk group was correlated with increased expression of ICOS. The high-risk group had a connection with lower half inhibitory centration (IC50) of most chemotherapy drugs (e.g., etoposide, paclitaxel, vinorelbine, gemcitabine, and docetaxel) and targeted medicine-erlotinib, but with higher IC50 of methotrexate.ConclusionThe established irlncRNA pairs-based model is a promising prognostic signature for LUAD patients. Furthermore, the prognostic signature has great potential in the evaluation of tumor immune landscape and guiding individualized treatment regimens.

Project description:BackgroundLung cancer has been the focus of attention for many researchers in recent years due to its leading contribution to cancer-related death worldwide, with lung adenocarcinoma (LUAD) being the most common histological type. Ferroptosis, a novel iron-dependent form of regulated cell death, can be induced by sorafenib. Emerging evidence shows that triggering ferroptosis has potential as a cancer therapy. This work aimed to build a ferroptosis-related gene signature for predicting the outcome of LUAD.MethodsThe TCGA-LUAD dataset was set as the training cohort, and the GSE72094 and GSE68465 datasets were set as the validation cohorts. Sixty-two ferroptosis-related genes were retrieved from the literature. A univariate Cox regression model was constructed for the training cohort to preliminarily screen for potential prognostic ferroptosis-related genes. A gene signature was generated from a LASSO Cox regression model and assessed with the training and validation cohorts through Kaplan-Meier, Cox, and ROC analyses. In addition, the correlation between the risk score and autophagy-related genes was determined by the Pearson test. Finally, GSEA and immune infiltrating analyses were performed to better study the functional annotation of the signature and the role of each kind of immune cell.ResultsA ten-gene signature was constructed from the training cohort and validated in three cohorts by Kaplan-Meier and Cox regression analyses, revealing its independent prognostic value in LUAD. Moreover, a ROC analysis conducted with all cohort data confirmed the predictive ability of the ten-gene signature for LUAD prognosis. A total of 62.85% (308/490) of autophagy-related genes were found to be significantly correlated with risk scores. GSEA detailed the exact pathways related to the gene signature, and immune-infiltrating analyses identified crucial roles for resting mast cells and resting dendritic cells in the prognosis of LUAD.ConclusionsWe identified a novel ferroptosis-related ten-gene signature (PHKG2, PGD, PEBP1, NCOA4, GLS2, CISD1, ATP5G3, ALOX15, ALOX12B, and ACSL3) that can accurately predict LUAD prognosis and is closely linked to resting mast cells and resting dendritic cells.

Project description:Background: Long non-coding RNAs (lncRNAs) are drawing increasing attention as promising predictors of prognosis for lung adenocarcinoma (LUAD) patients. Necroptosis, a novel regulated mechanism of necrotic cell death, plays an important role in the biological process of cancer. The aim of this study was to identify the necroptosis-related lncRNAs (NRLRs) in a LUAD cohort and establish a necroptosis-related lncRNA signature (NRLSig) to stratify LUAD patients. Methods: NRLRs were identified in LUAD patients from The Cancer Genome Atlas (TCGA) database using Pearson correlation analysis between necroptosis-related genes and lncRNAs. Then the NRLSig was identified using univariate Cox regression analysis and LASSO regression analysis. Assessments of the signature were performed based on survival analysis, receiver operating characteristic (ROC) curve analysis and clustering analysis. Next, a nomogram containing the NRLSig and clinical information was developed through univariate and multivariate Cox regression analysis. Further, functional enrichment analysis of the selected lncRNAs in NRLSig and the association between NRLSig and the immune infiltration were also evaluated. Results: A 4-lncRNA signature, incorporating LINC00941, AP001453.2, AC026368.1, and AC236972.3, was identified to predict overall survival (OS) and stratify LUAD patients into different groups. Survival analysis, ROC curve analysis and clustering analysis showed good performance in the prognostic prediction of the lncRNA signature. Then, a nomogram containing the NRLSig was developed and showed satisfactory predictive accuracy, calibration and clinical usefulness. The co-expressed genes of selected NRLRs were enriched in several biological functions and signaling pathways. Finally, differences in the abundance of immune cells were investigated among the high-risk group and low-risk group divided by the NRLSig. Conclusion: The proposed NRLSig may provide promising therapeutic targets or prognostic predictors for LUAD patients.

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Novel prognostic signature for lung adenocarcinoma based on immune-related mRNA pairs.

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Novel prognostic signature for lung adenocarcinoma based on immune-related mRNA pairs.

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