Prediction of prognosis and immunotherapy response with a robust immune-related lncRNA pair signature in lung adenocarcinoma

Cao, Kui; Liu, Mingdong; Ma, Keru; Jiang, Xuxian; Ma, Jianqun; Zhu, Jinhong

doi:10.1007/s00262-021-03069-1

Cited by 19 publications

(17 citation statements)

References 47 publications

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“…To evaluate different IRGs expression for individual patients, a quanti cation scoring model termed IRGriskscore was adopted by LASSO algorithm. Previous studies have also attempted to construct an immune-related gene prognostic index to distinguish the molecular and immune characteristics in various solid tumors [41][42][43]. For the associations of IRGriskscore with IRGcluster and IRG-genecluster, we found the IRGcluster-B and IRG-genecluster B subtype exhibited a lower IRGriskscore.…”

Section: Discussionmentioning

confidence: 74%

Combination of Immune-Related Network and Subtype-Specific Analysis Define a Three- Gene Panel for Predicting Prognosis of Triple-Negative Breast Cancer

Zhang

Pan

Jin

et al. 2022

Preprint

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Background: Recent breakthroughs in immune checkpoint inhibitors (ICIs) have shown promise in triple-negative breast cancer (TNBC). Due to the intrinsic heterogeneity among TNBC, clinical response to ICIs varies greatly among individuals. Thus, discovering rational biomarkers to select susceptible patients for ICIs treatment is warranted.Methods: A total of 422 TNBC patients derived from The Cancer Genome Atlas (TCGA) database and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset were included in this study. High immunogenic gene modules were identified using weighted gene coexpression network analysis (WGCNA). Immune-related genes (IRGs) expression patterns were generated by consensus clustering. We developed a three-gene signature named immune-related gene panel (IRGP) by Cox regression method. Afterward, the associations of IRGP with survival outcomes, infiltration of immune cells, drug sensitivity and the response to ICIs therapy were further explored.Results: We found five high immunogenic gene modules. Two distinct IRGclusters and IRG-related genomic clusters were identified. The IRGP was constructed based on TAPBPL, FBP1, and GPRC5C genes. TNBC patients were subdivided into high- and low-IRGriskscore subgroups. TNBC patients with low IRGriskscore had a better survival outcome, higher infiltration of immune cells, lower TP53 mutation rate and more benefit from ICIs treatment than high IRGriskscore patients.Conclusions: These findings offered novel insights into molecular subtype of TNBC and provided potential indicators for guiding ICIs treatment.

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Section: Discussionmentioning

confidence: 74%

Combination of Immune-Related Network and Subtype-Specific Analysis Define a Three- Gene Panel for Predicting Prognosis of Triple-Negative Breast Cancer

Zhang

Pan

Jin

et al. 2022

Preprint

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“…Furthermore, studies are required to validate molecular mechanisms between two risk groups in NSCLC. Moreover, the previous studies showed that the prognostic signature could be used to guide immunotherapy choices ( Cao et al, 2022 ). However, there was not a significant correlation between the risk scores and levels of immune checkpoint inhibitors expression.…”

Section: Discussionmentioning

confidence: 99%

Construction of a redox-related gene signature for overall survival prediction and immune infiltration in non-small-cell lung cancer

Miao

Yang

Gao

et al. 2022

Front. Mol. Biosci.

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Background: An imbalance in the redox homeostasis has been reported in multiple cancers and is associated with a poor prognosis of disease. However, the prognostic value of redox-related genes in non-small-cell lung cancer (NSCLC) remains unclear.Methods: RNA sequencing data, DNA methylation data, mutation, and clinical data of NSCLC patients were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. Redox-related differentially expressed genes (DEGs) were used to construct the prognostic signature using least absolute shrinkage and selection operator (LASSO) regression analysis. Kaplan–Meier survival curve and receiver operator characteristic (ROC) curve analyses were applied to validate the accuracy of the gene signature. Nomogram and calibration plots of the nomogram were constructed to predict prognosis. Pathway analysis was performed using gene set enrichment analysis. The correlations of risk score with tumor stage, immune infiltration, DNA methylation, tumor mutation burden (TMB), and chemotherapy sensitivity were evaluated. The prognostic signature was validated using GSE31210, GSE26939, and GSE68465 datasets. Real-time polymerase chain reaction (PCR) was used to validate dysregulated genes in NSCLC.Results: A prognostic signature was constructed using the LASSO regression analysis and was represented as a risk score. The high-risk group was significantly correlated with worse overall survival (OS) (p < 0.001). The area under the ROC curve (AUC) at the 5-year stage was 0.657. The risk score was precisely correlated with the tumor stage and was an independent prognostic factor for NSCLC. The constructed nomogram accurately predicted the OS of patients after 1-, 3-, and 5-year periods. DNA replication, cell cycle, and ECM receptor interaction were the main pathways enriched in the high-risk group. In addition, the high-risk score was correlated with higher TMB, lower methylation levels, increased infiltrating macrophages, activated memory CD4+ T cells, and a higher sensitivity to chemotherapy. The signature was validated in GSE31210, GSE26939, and GSE68465 datasets. Real-time PCR validated dysregulated mRNA expression levels in NSCLC.Conclusions: A prognostic redox-related gene signature was successfully established in NSCLC, with potential applications in the clinical setting.

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“…If we need to do more than one dataset validation, it can help us solve the problem of batch correction. Some IRLPs may be specified as constant values (0 or 1) in the dataset for the following reasons [ 42 ]: (1) bias caused by a particular platform; (2) biologically preferred transcription characteristics, this does not distinguish the survival of one patient from that of another. No relationship was considered between pairs and prognosis if the expression quantity of lncRNA pairs was 0 or 1 because pairs without a certain rank could not properly predict patient survival outcome [ 43 ].…”

Section: Discussionmentioning

confidence: 99%

A novel immune-related lncRNA pair signature for prognostic prediction and immune response evaluation in gastric cancer: a bioinformatics and biological validation study

Wang

Zhou

et al. 2022

Cancer Cell Int

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Background Gastric cancer (GC), the most commonly diagnosed cancer worldwide with poor 5-year survival rate in advanced stages. Although immune-related and survival-related biomarkers, which typically comprise aberrantly expressed long non-coding RNAs (lncRNAs) and genes, have been identified, there are no reports of immune-related lncRNA pair (IRLP) signatures for GC. Methods In this study, we acquired lncRNA expression profiles from The Cancer Genome Atlas (TCGA) and used the least absolute shrinkage and selection operator (LASSO) Cox proportional hazards model (iteration = 1000) to develop a IRLP prognostic signature. The area under curve (AUC) was used to assess the prognosis predictive power. The multivariate Cox regression analysis was performed to identify whether this signature was an independent prognostic factor. The immune cell infiltration analysis was performed between the two risk groups. Last, molecular experiments were performed to explore LINC01082 is involved in the development of GC. Results We acquired lncRNA expression profiles and used the LASSO Cox model to develop an 18-IRLP signature with a strong prognostic predictive power. The 5-year AUC values of the training, validation, and overall TCGA datasets were 0.77, 0.86, and 0.80, respectively. The different prognostic outcomes between the high- and low-risk groups were determined using our 18-IRLP signature. Moreover, our 18-IRLP signature was an independent prognostic factor as per the multivariate Cox regression analysis, and showed better prognostic evaluation than the traditional TNM staging system as well as other clinical features. We also found differences in cancer-associated fibroblast and macrophage M2 infiltration and the expression of PD-L1, CTLA4, LAG3, and HLA were also observed between the two risk groups (P < 0.05). Analysis of biological functions revealed that target genes of the lncRNAs in the IRLP signature were enriched in focal adhesion and regulation of actin cytoskeleton. Finally, as one of significant candidates of IRLP signature, overexpression of LINC01082 suppressed the invasion ability of GC cells as well as PD-L1 expression profiles. Conclusions Our novel 18-IRLP signature provides new insights regarding immunological biomarkers, imparts a better understanding of the tumor immune microenvironment, and can be used for predicting prognosis and evaluating immune response in GC.

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Prediction of prognosis and immunotherapy response with a robust immune-related lncRNA pair signature in lung adenocarcinoma

Cited by 19 publications

References 47 publications

Combination of Immune-Related Network and Subtype-Specific Analysis Define a Three- Gene Panel for Predicting Prognosis of Triple-Negative Breast Cancer

Combination of Immune-Related Network and Subtype-Specific Analysis Define a Three- Gene Panel for Predicting Prognosis of Triple-Negative Breast Cancer

Construction of a redox-related gene signature for overall survival prediction and immune infiltration in non-small-cell lung cancer

A novel immune-related lncRNA pair signature for prognostic prediction and immune response evaluation in gastric cancer: a bioinformatics and biological validation study

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