Prognostic model and immunotherapy prediction based on molecular chaperone-related lncRNAs in lung adenocarcinoma.

Introduction: Molecular chaperones and long non-coding RNAs (lncRNAs) have been confirmed to be closely related to the occurrence and development of tumors, especially lung cancer. Our study aimed to construct a kind of molecular chaperone-related long non-coding RNAs (MCRLncs) marker to accurately predict the prognosis of lung adenocarcinoma (LUAD) patients and find new immunotherapy targets. Methods: In this study, we acquired molecular chaperone genes from two databases, Genecards and molecular signatures database (MsigDB). And then, we downloaded transcriptome data, clinical data, and mutation information of LUAD patients through the Cancer Genome Atlas (TCGA). MCRLncs were determined by Spearman correlation analysis. We used univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis to construct risk models. Kaplan-meier (KM) analysis was used to understand the difference in survival between high and low-risk groups. Nomogram, calibration curve, concordance index (C-index) curve, and receiver operating characteristic (ROC) curve were used to evaluate the accuracy of the risk model prediction. In addition, we used gene ontology (GO) enrichment analysis and kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses to explore the potential biological functions of MCRLncs. Immune microenvironmental landscapes were constructed by using single-sample gene set enrichment analysis (ssGSEA), tumor immune dysfunction and exclusion (TIDE) algorithm, "pRRophetic" R package, and "IMvigor210" dataset. The stem cell index based on mRNAsi expression was used to further evaluate the patient's prognosis. Results: Sixteen MCRLncs were identified as independent prognostic indicators in patients with LUAD. Patients in the high-risk group had significantly worse overall survival (OS). ROC curve suggested that the prognostic features of MCRLncs had a good predictive ability for OS. Immune system activation was more pronounced in the high-risk group. Prognostic features of the high-risk group were strongly associated with exclusion and cancer-associated fibroblasts (CAF). According to this prognostic model, a total of 15 potential chemotherapeutic agents were screened for the treatment of LUAD. Immunotherapy analysis showed that the selected chemotherapeutic drugs had potential application value. Stem cell index mRNAsi correlates with prognosis in patients with LUAD. Conclusion: Our study established a kind of novel MCRLncs marker that can effectively predict OS in LUAD patients and provided a new model for the application of immunotherapy in clinical practice.

Xu Y ，Tao T ，Li S ，Tan S ，Liu H ，Zhu X ... -  《Frontiers in Genetics》

Tumor necrosis factor-related lncRNAs predict prognosis and immunotherapy response for patients with lung adenocarcinoma.

Lung adenocarcinoma (LUAD) has become one of the most lethal cancers, for which the recurrence and survival rates remain unfavorable. The tumor necrosis factor (TNF) family is involved in tumorigenesis and tumor progression. Various long non-coding RNAs (lncRNAs) play important roles by mediating the TNF family in cancer. Therefore, this study aimed to construct a TNF-related lncRNA signature to predict prognosis and immunotherapy response in LUAD. The expression of TNF family members and their related lncRNAs in a total of 500 enrolled LUAD patients was collected from The Cancer Genome Atlas (TCGA). Univariate Cox and the least absolute shrinkage and selection operator (LASSO)-Cox analysis was used to construct a TNF family-related lncRNA prognostic signature. Kaplan-Meier (KM) survival analysis was used to evaluate survival status. The time-dependent area under the receiver operating characteristic (ROC) curve (AUC) values were used to assess the predictive value of the signature to 1-, 2-, and 3-year overall survival (OS). Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were applied to identify the signature-related biological pathways. Furthermore, tumor immune dysfunction and exclusion (TIDE) analysis was employed to evaluate immunotherapy response. A total of 8 TNF-related lncRNAs significantly associated with OS of LUAD patients were used to construct a TNF family-related lncRNA prognostic signature. According to risk score, these patients were divided into high- and low-risk subgroups. The KM survival analysis indicated that patients in the high-risk group showed significantly less favorable OS than that of low-risk group. The AUC values in predicting 1-, 2-, and 3-year OS were 0.740, 0.738, and 0.758, respectively. Moreover, the GO and KEGG pathway analyses demonstrated that these lncRNAs were closely involved in immune-related signaling pathways. The further TIDE analysis indicated that high-risk patients had a lower TIDE score than that of low-risk patients, indicating that high-risk patients may be appropriate candidates for immunotherapy. For the first time, this study constructed and validated a prognostic predictive signature of LUAD patients based on TNF-related lncRNAs, and the signature showed good performance to predict immunotherapy response. Therefore, this signature may provide new strategies for individualized treatment of LUAD patients.

Chen JH ，Wu X ，Wang ZM ，Liu ZY ，He BX ，Song WP ，Zhang WZ ... -  《-》

Construction of a Prognostic Immune-Related LncRNA Risk Model for Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) originates mainly from the mucous epithelium and glandular epithelium of the bronchi. It is the most common pathologic subtype of non-small cell lung cancer (NSCLC). At present, there is still a lack of clear criteria to predict the efficacy of immunotherapy. The 5-year survival rate for LUAD patients remains low. All data were downloaded from The Cancer Genome Atlas (TCGA) database. We used Gene Set Enrichment Analysis (GSEA) database to obtain immune-related mRNAs. Immune-related lncRNAs were acquired by using the correlation test of the immune-related genes with R version 3.6.3 (Pearson correlation coefficient cor = 0.5, P < 0.05). The TCGA-LUAD dataset was divided into the testing set and the training set randomly. Based on the training set to perform univariate and multivariate Cox regression analyses, we screened prognostic immune-related lncRNAs and given a risk score to each sample. Samples were divided into the high-risk group and the low-risk group according to the median risk score. By the combination of Kaplan-Meier (KM) survival curve, the receiver operating characteristic (ROC) (AUC) curve, the independent risk factor analysis, and the clinical data of the samples, we assessed the accuracy of the risk model. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the differentially expressed mRNAs between the high-risk group and the low-risk group. The differentially expressed genes related to immune response between two risk groups were analyzed to evaluate the role of the model in predicting the efficacy and effects of immunotherapy. In order to explain the internal mechanism of the risk model in predicting the efficacy of immunotherapy, we analyzed the differentially expressed genes related to epithelial-mesenchymal transition (EMT) between two risk groups. We extracted RNA from normal bronchial epithelial cell and LUAD cells and verified the expression level of lncRNAs in the risk model by a quantitative real-time polymerase chain reaction (qRT-PCR) test. We compared our risk model with other published prognostic signatures with data from an independent cohort. We transfected LUAD cell with siRNA-LINC0253. Western blot analysis was performed to observed change of EMT-related marker in protein level. Through univariate Cox regression analysis, 24 immune-related lncRNAs were found to be strongly associated with the survival of the TCGA-LUAD dataset. Utilizing multivariate Cox regression analysis, 10 lncRNAs were selected to establish the risk model. The K-M survival curves and the ROC (AUC) curves proved that the risk model has a fine predictive effect. The GO enrichment analysis indicated that the effect of the differentially expressed genes between high-risk and low-risk groups is mainly involved in immune response and intercellular interaction. The KEGG enrichment analysis indicated that the differentially expressed genes between high-risk and low-risk groups are mainly involved in endocytosis and the MAPK signaling pathway. The expression of genes related to the efficacy of immunotherapy was significantly different between the two groups. A qRT-PCR test verified the expression level of lncRNAs in LUAD cells in the risk model. The AUC of ROC of 5 years in the independent validation dataset showed that this model had superior accuracy. Western blot analysis verified the change of EMT-related marker in protein level. The immune lncRNA risk model established by us could better predict the prognosis of patients with LUAD.

Li Y ，Shen R ，Wang A ，Zhao J ，Zhou J ，Zhang W ，Zhang R ，Zhu J ，Liu Z ，Huang JA ... -  《Frontiers in Cell and Developmental Biology》

Exploring the Prognostic Significance and Immunotherapeutic Potential of Single-Cell Sequencing-Identified Long Noncoding RNA (LncRNA) in Patients With Non-small Cell Lung Cancer.

Chen L ，Wang L ，Xiong Z ，Zhu X ，Chen L ... -  《Cureus》

Identification of a novel defined inflammation-related long noncoding RNA signature contributes to predicting prognosis and distinction between the cold and hot tumors in bladder cancer.

Bladder cancer (BLCA) is one of the most frequently diagnosed urological malignancies and is the 4th most common cancer in men worldwide. Molecular targets expressed in bladder cancer (BLCA) are usually used for developing targeted drug treatments. However, poor prognosis and poor immunotherapy efficacy remain major challenges for BLCA. Numerous studies have shown that long non-coding RNAs (LncRNAs) play an important role in the development of cancer. However, the role of lncRNAs related to inflammation in BLCA and their prognostic value remain unclear. Therefore, this study is aimed to explore new potential biomarkers that can predict cancer prognosis. We downloaded BLCA-related RNA sequencing data from The Cancer Genome Atlas (TCGA) and searched for inflammation-related prognostic long non-coding RNAs (lncRNAs) by univariate Cox (uniCox) regression and co-expression analysis. We used the least absolute shrinkage and selection operator (LASSO) analysis to construct an inflammation-related lncRNA prognosis risk model. Samples were divided into high-risk score (HRS) group and low-risk score (LRS) group based on the median value of risk scores. The independent variable factors were identified by univariate Cox (uni-Cox) and multivariate Cox (multi-Cox) regression analyses, and receiver operating characteristic (ROC) curves were used to compare the role of different factors in predicting outcomes. Nomogram and Calibration Plot were generated by the R package rms to analyze whether the prediction results are correct and show good consistency. Correlation coefficients were calculated by Pearson analysis. The Kaplan-Meier method was used to assess the prognostic value. The expression of 7 lncRNAs related with inflammation was also confirmed by qRT-PCR in BLCA cell lines. Kyoto Encyclopedia of Gene and Genome (KEGG) pathways that were significantly enriched (P < 0.05) in each risk group were identified by the GSEA software. The R package pRRophetic was used to predict the IC50 of common chemotherapeutic agents. TIMER, XCELL, QUANTISEQ, MCPCOUNTER, EPIC and CIBERSORT were applied to quantify the relative proportions of infiltrating immune cells. We also used package ggpubr to evaluate TME scores and immune checkpoint activation in LRS and HRS populations. R package GSEABase was used to analyze the activity of immune cells or immune function. Different clusters of principal component analysis (PCA), t-distribution random neighborhood embedding (t-SNE), and Kaplan-Meier survival were analyzed using R package Rtsne's. The R package ConsensesClusterPlus was used to class the inflammation-related lncRNAs. In this study, a model containing 7 inflammation-related lncRNAs was constructed. The calibration plot of the model was consistent with the prognosis prediction outcomes. The 1-, 3-, and 5-year ROC curve (AUC) were 0.699, 0.689, and 0.699, respectively. High-risk patients were enriched in lncRNAs related with tumor invasion and immunity, and had higher levels of immune cell infiltration and immune checkpoint activation. Hot tumors and cold tumors were effectively distinguished by clusters 2 and 3 and cluster 1, respectively, which indicated that hot tumors are more susceptible to immunotherapy. Our study showed that inflammation-related LncRNAs are closely related with BLCA, and inflammation-related lncRNA can accurately predict patient prognosis and effectively differentiate between hot and cold tumors, thus improving individualized immunotherapy for BLCA patients. Therefore, this study provides an effective predictive model and a new therapeutic target for the prognosis and clinical treatment of BLCA, thus facilitating the development of individualized tumor therapy.

Xiong X ，Chen C ，Li X ，Yang J ，Zhang W ，Wang X ，Zhang H ，Peng M ，Li L ，Luo P ... -  《Frontiers in Oncology》