Immune Subtypes Based on Immune-Related lncRNA: Differential Prognostic Mechanism of Pancreatic Cancer.
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ABSTRACT:
SUBMITTER: Zhang Q
PROVIDER: S-EPMC8292792 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundPancreatic cancer (PC) has a high mortality and dismal prognosis, predicting to be the second most lethal malignancy. 5-Methylcytosine (m5C) and long noncoding RNAs (lncRNAs) are both crucial in the prognostic outcome and immunotherapeutic effect for PC patients. Therefore, we aimed to create an m5C-related lncRNA signature (m5C-LS) for PC patients' prognosis and treatment.MethodsClinicopathological information and RNAseq data were acquired from The Cancer Genome Atlas (TCGA) database. Pearson's correlation analysis was used to extract m5C-related lncRNAs in PC. Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox analyses were adopted to build an m5C-LS. Kaplan-Meier (K-M), principal component analysis (PCA), and nomogram were utilized to assess model accuracy. In addition, we explored the model's possible immunotherapeutic responses and drug sensitivity targets.ResultsThree m5C-related lncRNAs were finally established to construct the risk signature, which has a good and independent predictive ability for PC patients. Based on the m5C-LS, patients were classified into the low- and high-m5C-LS group, with the latter having a worse prognosis. Furthermore, the m5C-LS allowed us to better discriminate the immunotherapeutic responses of PC patients in different subgroups.ConclusionsOur study constructed an m5C-LS and established a nomogram model that accurately predicted the prognosis of PC patients, as well as provides promising immunotherapeutic strategies in the future.
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Project description:BackgroundGlioblastoma multiforme (GBM) is extensively genetically and transcriptionally heterogeneous, which poses challenges for classification and management. Long noncoding RNAs (lncRNAs) play a critical role in the development and progression of GBM, especially in tumor-associated immune processes. Therefore, it is necessary to develop an immune-related lncRNAs (irlncRNAs) signature.MethodsUnivariate and multivariate Cox regression analyses were utilized to construct a prognostic model. GBM-specific CeRNA and PPI network was constructed to predict lncRNAs targets and evaluate the interactions of immune mRNAs translated proteins. GO and KEGG pathway analyses were used to show the biological functions and pathways of CeRNA network-related immunity genes. Consensus Cluster Plus analysis was used for GBM gene clustering. Then, we evaluated GBM subtype-specific prognostic values, clinical characteristics, genes and pathways, immune infiltration access single cell RNA-seq data, and chemotherapeutics efficacy. The hub genes were finally validated.ResultsA total of 17 prognostically related irlncRNAs were screened to build a prognostic model signature based on six key irlncRNAs. Based on GBM-specific CeRNAs and enrichment analysis, PLAU was predicted as a target of lncRNA-H19 and mainly enriched in the malignant related pathways. GBM subtype-A displayed the most favorable prognosis, high proportion of genes (IDH1, ATRX, and EGFR) mutation, chemoradiotherapy, and low risk and was characterized by low expression of four high-risk lncRNAs (H19, HOTAIRM1, AGAP2-AS1, and AC002456.1) and one mRNA KRT8. GSs with poor survival were mainly infiltrated by mesenchymal stem cells (MSCs) and astrocyte, and were more sensitive to gefitinib and roscovitine. Among GSs, three hub genes KRT8, NGFR, and TCEA3, were screened and validated to potentially play feasible oncogenic roles in GBM.ConclusionConstruction of lncRNAs risk model and identification of GBM subtypes based on 17 irlncRNAs, which suggesting that irlncRNAs had the promising potential for clinical immunotherapy of GBM.
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Project description:Long noncoding RNAs (lncRNAs) play a critical role in the immune regulation and tumor microenvironment of pancreatic cancer (PaCa). To construct a novel immune-related prognostic risk model for PaCa and evaluate the prognostic prediction of lncRNAs, essential immune-related lncRNAs (IRlncRNAs) were identified by Pearson correlation analysis of differentially expressed immune-related genes (IRGs) and IRlncRNAs in PaCa from The Cancer Genome Atlas (TCGA) and GTEx databases. Least absolute shrinkage and selection operator (LASSO) regression was also applied to construct a prognostic risk model of IRlncRNAs, and gene set enrichment analysis (GSEA) was further applied for functional annotation for these IRlncRNAs. A total of 148 IRlncRNAs were identified in PaCa to construct a prognostic risk model. Among them, lncRNA LINC02325, FNDC1-AS1, and ZEB2-AS1 were significantly upregulated in 69 pairs of PaCa tissues by qRT-PCR. ROC analyses showed that LINC02325 (AUC = 0.80), FNDC1-AS1 (AUC = 0.76), and ZEB2-AS1 (AUC = 0.75) had a good predictive effect on 5-year survival prognosis. We demonstrated that high expression levels of ZEB2-AS1 and LINC02325 were not only positively associated with tumor size and CA199, but elevated levels of ZEB2-AS1 and FNDC1-AS1 were also positively correlated with tumor stage. GSEA further revealed that immune-related pathways were mainly enriched in the high-risk groups. Several immune-related algorithms demonstrated that four IRlncRNAs were related to immune infiltration, immune checkpoints, and immune-related functions. Thus, the prognostic risk model based on IRlncRNAs in Paca indicates that the four IRlncRNA signatures may serve as predictors of survival and potential predictive biomarkers of the pancreatic tumor immune response.
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Project description:BackgroundGrowing evidence has suggested that enhancer RNAs (eRNAs), a set of long non-coding RNAs (lncRNAs) that were derived from active enhancer regions, play critical roles in regulating gene expression in human cancers. Nevertheless potential functions of eRNAs in esophageal cancer ESCA have not yet been expounded. Here, this study aimed to explore key prognostic eRNAs in ESCA.MethodsLncRNAs that were transcribed from active enhancer regions were analyzed utilizing the PreSTIGE algorithm, followed by prediction of their target genes. Based on the ESCA RNA-seq data from the TANRIC database, overall survival (OS)-related eRNAs were determined. The correlation between AC007255.1 expression and various clinical traits of ESCA was calculated. Functional enrichment analysis was presented based on its co-expressed genes. Based on the TIMER database, we analyzed correlations between AC007255.1 expression and immune infiltration levels. qRT-PCR was utilized to validate the expression of AC007255.1 and PRR15 in ESCA and normal tissues.ResultsTotally, 2,695 lncRNAs were transcribed from active enhancer regions. Among them, 33 were significantly related to OS. AC007255.1 was a key eRNA. PRR15 was a target gene of AC007255.1 (correlation coefficient r = 0.936). Patients with high AC007255.1 expression indicated poor OS time. There were significant correlations between AC007255.1 expression and clinical characteristics like pathological TNM, grade and stage. AC007255.1 was closely related to tight junction and neutrophil activation involved in immune response. Moreover, AC007255.1 expression was related to the infiltration levels of B cell, dendritic cell and neutrophil. qRT-PCR results confirmed that AC007255.1 and PRR15 were both up-regulated in ESCA tissues, and there was a positive correlation between the two.ConclusionOur findings identified a novel immune-related eRNA AC007255.1 in ESCA, which could be a promising prognostic factor for ESCA.
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Project description:Immunotherapy has a good therapeutic effect and provides a new approach for cancer treatment. However, only limited studies have focused on the use of molecular typing to construct an immune characteristic index for gene expression in pancreatic adenocarcinoma (PAAD) and to assess the effectiveness of immunotherapy in patients with PAAD. Clinical follow-up data and gene expression profile of PAAD patients were retrieved from The Cancer Genome Atlas (TCGA) database. Based on 184 immune features, molecular subtypes of pancreatic cancer were found by the "ConsensusClusterPlus" package, and the association between clinical features and immune cell subtype distribution was analysed. In addition, the relationship between the proportion of immune subtypes and the expression of immune checkpoints was analysed. The CIBERSORT algorithm was introduced to evaluate the immune scores of different molecular subtypes. We used the tumor immune dysfunction and exclusion (TIDE) algorithm to assess the potential clinical effect of immunotherapy interventions on single-molecule subtypes. In addition, the oxidative stress index was constructed by linear discriminant analysis DNA (LDA), and weighted correlation network analysis was performed to identify the core module of the index and its characteristic genes. Expression of hub genes was verified by immunohistochemical analysis in an independent PAAD cohort. Pancreatic cancer is divided into three molecular subtypes (IS1, IS2, and IS3), with significant differences in prognosis between multiple cohorts. Expression of immune checkpoint-associated genes was significantly reduced in IS3 and higher in IS1 and IS2, suggesting that the three subgroups have different responsiveness to immunotherapy interventions. The results of the CIBERSORT analysis showed that IS1 exhibited the highest levels of immune infiltration, whereas the results of the TIDE analysis showed that the T-cell dysfunction score of IS1 was higher than that of IS2 and IS3. Furthermore, IS3 was found to be more sensitive to 5-FU and to have a higher immune signature index than IS1 and IS2. Based on WGCNA analysis, 10 potential gene markers were identified, and their expression at the protein level was verified by immunohistochemical analysis. Specific molecular expression patterns in pancreatic cancer can predict the efficacy of immunotherapy and influence the prognosis of patients.
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Project description:Breast cancer (BC) is a prominent cause of cancer incidence and mortality around the world. Disulfidptosis, a type of cell death, can induce tumor cell death. The purpose of this study was to analyze the potential impact of disulfidptosis-related genes (DRGs) on the prognosis and immune infiltration features of BC. Based on DRGs, we conducted an unsupervised clustering analysis on gene expression data of BC in TCGA-BRCA dataset and identified two BC subtypes, cluster1 and cluster2, with cluster1 showing a higher likelihood of favorable survival. Through immune analysis, we found that cluster1 had lower proportions of infiltration in immune-related cells, including aDCs, DCs, NK_cells, Th2_cells, and Treg. Based on the immunophenoscore (IPS) results, we inferred that cluster1 might benefit more from immune checkpoint inhibitors targeting CTLA-4 and PD1. Targeted small molecule prediction results showed that patients with cluster2 BC might respond better to antagonistic small molecule compounds, including clofazimine, lenalidomide, and epigallocatechin. Differentially expressed genes between the two subtypes were found to be enriched in signaling pathways related to steroid hormone biosynthesis, ovarian steroidogenesis, and neutrophil extracellular trap formation, according to enrichment analyses. In conclusion, this study identified BC subtypes based on DRGs so as to help predict patient prognosis and provide valuable tools for guiding clinical management and precise treatment of BC patients.
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Project description:BackgroundThe characteristics of immune-related long non-coding ribonucleic acids (ir-lncRNAs), regardless of their specific levels, have important implications for the prognosis of patients with bladder cancer.MethodsBased on The Cancer Genome Atlas database, original transcript data were analyzed. The ir-lncRNAs were obtained using a coexpression method, and their differentially expressed pairs (DE-ir-lncRNAs) were identified by univariate analysis. The lncRNA pairs were verified using a Lasso regression test. Thereafter, receiver operating characteristic curves were generated, and an optimal risk model was established. The clinical value of the model was verified through the analysis of patient survival rates, clinicopathological characteristics, presence of tumor-infiltrating immune cells, and chemotherapy efficacy evaluation.ResultsIn total, 49 pairs of DE-ir-lncRNAs were identified, of which 21 were included in the Cox regression model. A risk regression model was established on the premise of not involving the specific expression value of the transcripts.ConclusionsThe method and model used in this study have important clinical predictive value for bladder cancer and other malignant tumors.
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Project description:This study collected immune-related genes (IRGs) and used gene expression data from TCGA database to construct a molecular subtype of ovarian cancer (OV) based on immune-related lncRNA gene pairs (IRLnc_GPs). The relationships between molecular subtypes and prognosis and clinical characteristics were further explored. IRGs were acquired from the ImmPort database, and round-robin pairing of immune-related lncRNAs was performed. The NMF algorithm was used to identify molecular subtypes, and the immune score of a single sample was calculated through ESTIMATE, TIMER, ssGSEA, MCPcounter, and CIBERSORT. The relationship between molecular subtypes and immune microenvironments was identified. A hypergeometric test was used to test the lncRNA pairs among the OV molecular subtypes (C1 and C2 subtypes). The BH method was used to screen the different lncRNA pairs, and a predictive risk model was constructed and verified. Finally, correlation analysis between the risk model, immune checkpoint genes, and chemotherapy drugs was carried out. Based on IRLnc_GP to classify 373 OV samples of TCGA, the samples were divided into two subtypes, and the prognosis between the subtypes showed significant differences. The C1 subtype with a poor prognosis was more related to the pathways of tumor occurrence and development. We identified 180 differential lncRNA pairs between subtypes and constructed a prognostic risk model based on 8 IRLnc_GPs. In the independent dataset, the distribution of subtypes in functional modules was different and highly repeatable. There were significant differences in the molecular and clinical characteristics of the subtypes and the drug sensitivity of immunotherapy/chemotherapy. In conclusion, the risk model established based on IRLnc_GP can better evaluate the prognosis of OV samples and can also assess the effects of different drug treatments in the high- and low-risk groups, providing new insights and ideas for the treatment of OV.
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Project description:BackgroundN6-methyladenosine (m6A) related long noncoding RNAs (lncRNAs) may have prognostic value in bladder cancer for their key role in tumorigenesis and innate immunity.MethodsBladder cancer transcriptome data and the corresponding clinical data were acquired from the Cancer Genome Atlas (TCGA) database. The m6A-immune-related lncRNAs were identified using univariate Cox regression analysis and Pearson correlation analysis. A risk model was established using least absolute shrinkage and selection operator (LASSO) Cox regression analyses, and analyzed using nomogram, time-dependent receiver operating characteristics (ROC) and Kaplan-Meier survival analysis. The differences in infiltration scores, clinical features, and sensitivity to Talazoparib of various immune cells between low- and high-risk groups were investigated.ResultsTotally 618 m6A-immune-related lncRNAs and 490 immune-related lncRNAs were identified from TCGA, and 47 lncRNAs of their intersection demonstrated prognostic values. A risk model with 11 lncRNAs was established by Lasso Cox regression, and can predict the prognosis of bladder cancer patients as demonstrated by time-dependent ROC and Kaplan-Meier analysis. Significant correlations were determined between risk score and tumor malignancy or immune cell infiltration. Meanwhile, significant differences were observed in tumor mutation burden and stemness-score between the low-risk group and high-risk group. Moreover, high-risk group patients were more responsive to Talazoparib.ConclusionsAn m6A-immune-related lncRNA risk model was established in this study, which can be applied to predict prognosis, immune landscape and chemotherapeutic response in bladder cancer.
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Project description:Increasing evidence has demonstrated that lncRNAs are critical regulators in diverse biological processes, but the function of lncRNA in metabolic regulation remains largely unexplored. In this study, we evaluated the association between lncRNA and metabolic pathways and identified metabolism-related lncRNAs. Gastric cancer can be mainly subdivided into 2 clusters based on these metabolism-related lncRNA regulators. Comparative analysis shows that these subtypes are found to be highly consistent with previously identified subtypes based on other omics data. Functional enrichment analysis shows that they are enriched in distinct biological processes. Mutation analysis shows that ABCA13 is a protective factor in subtype C1 but a risk factor in C2. Analysis of chemotherapeutic and immunotherapeutic sensitivity shows that these subtypes tend to display distinct sensitivity to the same chemical drugs. In conclusion, these findings demonstrated the significance of lncRNA in metabolic regulation. These metabolism-related lncRNA regulators can improve our understanding of the underlying mechanism of lncRNAs and advance the research of immunotherapies in the clinical management of gastric cancer.
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