A Novel Necroptosis-Related lncRNA Signature Predicts the Prognosis of Lung Adenocarcinoma.

Background: Necroptosis is closely related to the tumorigenesis and development of cancer. An increasing number of studies have demonstrated that targeting necroptosis could be a novel treatment strategy for cancer. However, the predictive potential of necroptosis-related long noncoding RNAs (lncRNAs) in lung adenocarcinoma (LUAD) still needs to be clarified. This study aimed to construct a prognostic signature based on necroptosis-related lncRNAs to predict the prognosis of LUAD. Methods: We downloaded RNA sequencing data from The Cancer Genome Atlas database. Co-expression network analysis, univariate Cox regression, and least absolute shrinkage and selection operator were adopted to identify necroptosis-related prognostic lncRNAs. We constructed the predictive signature by multivariate Cox regression. Kaplan-Meier analysis, time-dependent receiver operating characteristics, nomogram, and calibration curves were used to validate and evaluate the signature. Subsequently, we used gene set enrichment analysis (GSEA) and single-sample gene set enrichment analysis (ssGSEA) to explore the relationship between the predictive signature and tumor immune microenvironment of risk groups. Finally, the correlation between the predictive signature and immune checkpoint expression of LUAD patients was also analyzed. Results: We constructed a signature composed of 7 necroptosis-related lncRNAs (AC026355.2, AC099850.3, AF131215.5, UST-AS2, ARHGAP26-AS1, FAM83A-AS1, and AC010999.2). The signature could serve as an independent predictor for LUAD patients. Compared with clinicopathological variables, the necroptosis-related lncRNA signature has a higher diagnostic efficiency, with the area under the receiver operating characteristic curve being 0.723. Meanwhile, when patients were stratified according to different clinicopathological variables, the overall survival of patients in the high-risk group was shorter than that of those in the low-risk group. GSEA showed that tumor- and immune-related pathways were mainly enriched in the low-risk group. ssGSEA further confirmed that the predictive signature was significantly related to the immune status of LUAD patients. The immune checkpoint analysis displayed that low-risk patients had a higher immune checkpoint expression, such as CTLA-4, HAVCR2, PD-1, and TIGIT. This suggested that immunological function is more active in the low-risk group LUAD patients who might benefit from checkpoint blockade immunotherapies. Conclusion: The predictive signature can independently predict the prognosis of LUAD, helps elucidate the mechanism of necroptosis-related lncRNAs in LUAD, and provides immunotherapy guidance for patients with LUAD.

Lu Y ，Luo X ，Wang Q ，Chen J ，Zhang X ，Li Y ，Chen Y ，Li X ，Han S ... -  《Frontiers in Genetics》

Cross-talk between necroptosis-related lncRNAs to construct a novel signature and predict the immune landscape of lung adenocarcinoma patients.

Background: As a new style of cell death, necroptosis plays a crucial role in tumor immune microenvironment. LncRNAs have been identified to act as competitive RNAs to influence genes involved in necroptosis. Therefore, we aim to create a signature based on necroptosis-related lncRNAs to predict the prognosis and immune landscape of lung adenocarcinoma (LUAD) patients in this study. Methods: TCGA database was used to acquire RNA sequencing (RNA-Seq) data and clinical information for 59 lung normal samples and 535 lung adenocarcinoma samples. The Pearson correlation analysis, univariate cox regression analysis and least absolute shrinkage and selection operator (LASSO) cox regression were performed to construct the prognostic NRlncRNAs signature. Then we used Kaplan-Meier (K-M) analysis, time-dependent ROC curves, univariate and multivariate cox regression analysis, and nomogram to validate this signature. In addition, GO, KEGG, and GSVA were analyzed to investigate the potential molecular mechanism. Moreover, we analyzed the relationship between our identified signature and immune microenvironment, TMB, and some clinical characteristics. Finally, we detected the expression of the six necroptosis-related lncRNAs in cells and tissues. Results: We constructed a NRlncRNAs signature consisting of six lncRNAs (FRMD6-AS1, LINC01480, FAM83A-AS1, FRMD6-AS1, MED4-AS1, and LINC01415) in LUAD. LUAD patients with high risk scores had lower chance of survival with an AUC of 0.739, 0.709, and 0.733 for 1-year, 3-year, and 5-year respectively. The results based on GO, KEGG, and GSVA enrichment analysis demonstrated that NRlncRNAs signature-related genes were mainly correlated with immune pathways, metabolic-and cell growth-related pathways, cell cycle, and apoptosis. Moreover, the risk score was correlated with the immune status of LUAD patients. Patients with higher risk scores had lower ESTIMATE scores and higher TIDE scores. The risk score was positively correlated with TMB. LINC01415, FRMD6-AS1 and FAM83A-AS1 were significantly overexpressed in lung adenocarcinoma, while the expression levels of MED4-AS1 and LINC01480 were lower in lung adenocarcinoma. Conclusion: Overall, an innovative prognostic signature based on NRlncRNAs was developed for LUAD through comprehensive bioinformatics analysis, which can act as a predictor of immunotherapy and may provide guidance for clinicians.

Wu J ，Song D ，Zhao G ，Chen S ，Ren H ，Zhang B ... -  《Frontiers in Genetics》

A New Ferroptosis-Related lncRNA Signature Predicts the Prognosis of Bladder Cancer Patients.

Background: Ferroptosis is closely related to the occurrence and development of cancer. An increasing number of studies have induced ferroptosis as a treatment strategy for cancer. However, the predictive value of ferroptosis-related lncRNAs in bladder cancer (BC) still need to be further elucidated. The purpose of this study was to construct a predictive signature based on ferroptosis-related long noncoding RNAs (lncRNAs) to predict the prognosis of BC patients. Methods: We downloaded RNA-seq data and the corresponding clinical and prognostic data from The Cancer Genome Atlas (TCGA) database and performed univariate and multivariate Cox regression analyses to obtain ferroptosis-related lncRNAs to construct a predictive signature. The Kaplan-Meier method was used to analyze the overall survival (OS) rate of the high-risk and low-risk groups. Gene set enrichment analysis (GSEA) was performed to explore the functional differences between the high- and low-risk groups. Single-sample gene set enrichment analysis (ssGSEA) was used to explore the relationship between the predictive signature and immune status. Finally, the correlation between the predictive signature and the treatment response of BC patients was analyzed. Results: We constructed a signature composed of nine ferroptosis-related lncRNAs (AL031775.1, AL162586.1, AC034236.2, LINC01004, OCIAD1-AS1, AL136084.3, AP003352.1, Z84484.1, AC022150.2). Compared with the low-risk group, the high-risk group had a worse prognosis. The ferroptosis-related lncRNA signature could independently predict the prognosis of patients with BC. Compared with clinicopathological variables, the ferroptosis-related lncRNA signature has a higher diagnostic efficiency, and the area under the receiver operating characteristic curve was 0.707. When patients were stratified according to different clinicopathological variables, the OS of patients in the high-risk group was shorter than that of those in the low-risk group. GSEA showed that tumor- and immune-related pathways were mainly enriched in the high-risk group. ssGSEA showed that the predictive signature was significantly related to the immune status of BC patients. High-risk patients were more sensitive to anti-PD-1/L1 immunotherapy and the conventional chemotherapy drugs sunitinib, paclitaxel, cisplatin, and docetaxel. Conclusion: The predictive signature can independently predict the prognosis of BC patients, provides a basis for the mechanism of ferroptosis-related lncRNAs in BC and provides clinical treatment guidance for patients with BC.

Chen M ，Nie Z ，Li Y ，Gao Y ，Wen X ，Cao H ，Zhang S ... -  《Frontiers in Cell and Developmental Biology》

Identification and Validation of a Novel Necroptosis-Related Long Noncoding RNA Prognostic Signature for Lung Adenocarcinoma.

Several cancers, including lung adenocarcinoma (LUAD), are caused by genes related to necroptosis. However, it is still unknown how necroptosis-related long noncoding RNAs (lncRNAs) may be involved in LUAD. In order to predict the prognosis of LUAD patients and personalize immunotherapy, this study set out to construct a necroptosis-related lncRNA prognostic signature (NLPS). The Cancer Genome Atlas (TCGA) database was used to download the LUAD transcriptome data and the associated clinical data. lncRNAs associated with necroptosis were screened using coexpression analysis. An NLPS was built using univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses. The Gene Expression Omnibus (GEO) database's GSE30219 was used to validate the NLPS. The prognostic value of the risk score was assessed using Kaplan-Meier survival, receiver operating characteristic (ROC) Cox regression, multivariate Cox regression, and nomogram analyses. Then, we looked into the differences between the low- and high-risk groups in the tumor immune microenvironment, immunotherapy response, and half-maximal inhibitory concentration (IC50). The 14 lncRNAs in a novel NLPS were created. With further validation in the GSE30219 dataset, the risk score according to the NLPS was an independent prognostic indicator for LUAD patients. Patients with better overall survival (OS) in the low-risk group, who were characterized by increased immune cell infiltration, tumor mutational burden (TMB), and immunophenoscore (IPS), may have hot tumors and higher immunotherapy response rates. In addition, the risk score was also closely linked to sensitivity to various anticancer medications. We constructed a novel NLPS that could predict OS and sensitivity to immunotherapy in LUAD patients.

Zeng C ，Yu H ，Liu X ，Liu Q ，Jin J ... -  《-》

A methylation-related lncRNA-based prediction model in lung adenocarcinomas.

The collaboration between methylation and the lung adenocarcinoma (LUAD) occurrence and development is closes. Long noncoding RNA (lncRNA), as a regulatory factor of various biological functions, can be used for cancer diagnosis. Our study aimed to construct a robust methylation-related lncRNA signature of LUAD. In the Cancer Genome Atlas (TCGA) dataset, we download the RNA expression data and clinical information of LUAD cases. To develop the best prognostic signature based on methylation-related lncRNAs, Cox regression analyses were utilized. Using Kaplan-Meier analysis, overall survival rates were compared between risk category included both low- and high-risk patients. To categorize genes according to their functional significance, GSEA (Subramanian et al, 2005) was used. Single-sample gene set enrichment analysis (ssGSEA) was used to further reveal the potential molecular mechanism of the methylation-related lncRNA prognostic model in immune infiltration. Using TRLnc (http://www.licpathway.net/TRlnc) and lncRNASNP to analyse the SNP sites and TRLnc of these 18 lncRNAs. LncSEA website was used to analyse 18 lncRNA in the process of tumour development and development. Go was used to analyse the enriched pathways enriched by TFs (transcription factors), Cerna networks, and proteins bound to each other of these 18 lncRNAs. The 'prophetic' package was used to analyse the value of this prognostic model in guiding personalized immunotherapy. In this study, we identified 18 methylation-related lncRNAs (AP002761.1, AL118558.3, CH17-340M24.3, AL353150.1, AC004687.1, LINC00996, AF186192.1, HSPC324, AC087752.3, FAM30A, AC106047.1, AC026355.1, ABALON, LINC01843, AL606489.1, NKILA, AP001453.2, GSEC) to establish a methylation-related lncRNA signature that can detect patients prognosis in LUAD. The enriched pathways enriched by proteins interacting with 18 lncRNAs are mainly EMT, hypoxia, stemness and proliferation, among which LINC00996 and AF186192.1 are regulated by multiple tumour associated transcription factors, such as TP53 and TP63, and fam30a and mRNA form a Cerna network. There are 2319 SNP loci in LINC00996, 36 of which are risk SNP loci and 205 SNP loci in af186192.1; AF186192.1 affects 95 conserved miRNAs and 123 non-conserved miRNAs, promotes the binding of 149 pairs of miRNAs: lncRNAs and inhibits the binding of 95 pairs of miRNAs: lncRNAs. The ROC curve demonstrated that the established methylation-related lncRNA signature was more effective in predicting the prognosis of patients in LUAD than the clinicopathological parameters. Our research has confirmed that patients in the high-risk group which was separated by the risk score model based on methylation-related lncRNA had shorter OS. According to GSEA, the high-risk group had a predominantly tumour- and immune-related pathway enrichment. A significant association was shown by ssGSEA between predictive signature and immune status in LUAD patients. In addition, principal component analysis (PCA) demonstrated the prognostic and predictive value of our signature. The correlation between the predictive signature of methylation-related lncRNA and IC50 of conventional chemotherapy drugs can provide personalized chemotherapy regimens for LUAD patients. Methylation-related lncRNA signature can effectively predict DFS of patients in LUAD.

Yang K ，Liu H ，Li JH 《-》