Immune-related lncRNAs Research Articles

The joint role of methylation and immune-related lncRNAs in ovarian cancer: Defining molecular subtypes and developing prognostic signature

IntroductionComplex outcome of ovarian cancer (OC) stems from the tumor immune microenvironment (TIME) influenced by genetic and epigenetic factors. This study aimed to comprehensively explored the subclasses of OC through lncRNAs related to both N6-methyladenosine (m6A)/N1-methyladenosine (m1A)/N7-methylguanosine (m7G)/5-methylcytosine (m5C) in terms of epigenetic variability and immune molecules and develop a new set of risk predictive systems. Material and methodsThe lncRNA data of OC were collected from TCGA. Spearman correlation analysis on lncRNA data of OC with immune-related gene expression and with m6A/m5C/m1A/m7G were respectively conducted. The m6A/m5C/m1A/m7G-related m6A/m5C/m1A/m7G related immune lncRNA subtypes were identified on the basis of the prognostic lncRNAs. Heterogeneity among subtypes was evaluated by tumor mutation analysis, tumor microenvironment (TME) component analysis, response to immune checkpoint blocked (ICB) and chemotherapeutic drugs. A risk predictive system was developed based on the results of Cox regression analysis and random survival forest analysis of the differences between each specific cluster and other clusters. ResultsThree m6A/m5C/m1A/m7G-related immune lncRNA subtypes of OC showing distinct differences in prognosis, mutation pattern, TIME components, immunotherapy and chemotherapy response were identified. A set of risk predictive system consisting of 10 lncRNA for OC was developed, according to which the risk score of samples in each OC dataset was calculated and risk type was defined. ConclusionsThis study classified three m6A/m5C/m1A/m7G-related immune lncRNA subtypes with distinct heterogeneous mutation patterns, TME components, ICB therapy and immune response, and provided a set of risk predictive system consisted of 10 lncRNA for OC.

Immune-related lncRNAs pairs prognostic score model for prediction of survival in acute myeloid leukemia patients

Acute myeloid leukemia (AML) is one of the most common malignant and aggressive hematologic tumors, and risk stratification is indispensable to ensure proper treatment. But immune-related long noncoding RNAs (ir-lncRNAs) pairs prognostic risk models used to stratify AML have yet to be reported. In this study, we established a prognostic risk model based on eight ir-lncRNAs pairs using LASSO-penalized Cox regression analysis and successfully validated the model in an independent cohort. According to risk scores, patients were divided into a high-risk group and a low-risk group. High-risk patients presented more tumor mutation frequency and higher expression of human leukocyte antigen (HLA)-related genes and immune checkpoint molecules. Gene Set Enrichment Analysis (GSEA) indicated that the transforming growth factors β (TGFβ) pathway was activated in the high-risk group; meanwhile, we found that TGFβ1 mRNA levels were significantly elevated in AML patients and correlated with poor prognosis, which is closely related to drug resistance. Consistently, in vitro studies found that exogenous TGFβ1 can protect AML cells from chemotherapy-induced apoptosis. Collectively, we developed an ir-lncRNA prognostic model that helps predict the prognosis of AML patients and provides valuable information about their response to immune checkpoint inhibitors, and we found that increased TGFβ1 levels resulting in chemoresistance may be one of the leading causes of treatment failure in high-risk AML patients.

Insights from Long Noncoding RNAs into Cancer-immunity Cycle Regulation

Accumulating evidence from recent research offers new perspectives on the functions of long non-coding RNAs (lncRNAs) in immunooncology. In addition to modulating the aggressiveness of cancer cells, lncRNAs are essential players in regulating various immune cells and stromal cells, playing a role in reshaping the tumor microenvironment and affecting anti-tumor immunity. The insightful discoveries on the role of lncRNAs in immuno-oncological activities indicate the prognostic value of lncRNA markers. Here, we present an overview of the roles of lncRNAs derived from different cell types in the tumor microenvironment, that is, immune cells, tumor cells, and stromal cells, and summarize their functional characterization and mechanisms in immuno-oncological activities. We also discuss the opportunities and challenges of single-cell-based technologies for analyzing the cellular function of immune-related lncRNAs.

Competing endogenous RNA analysis identified lncRNA DSCR9 as a novel prognostic biomarker associated with metastasis and tumor microenvironment in renal cell carcinoma.

Distant metastasis is the main cause of death in patients with clear cell renal carcinoma (ccRCC). The dysregulation of the tumor microenvironment is responsible for tumorigenesis and metastasis in ccRCC. The role of long non-coding RNAs in the tumor immune of ccRCC remains unclear. The present study screened differentially expressed protein-coding genes and non-coding genes between ccRCC and normal tissues based on three datasets. The commonly deregulated genes were used to identify distant metastasis-related long non-coding RNAs (lncRNAs) and prognostic lncRNAs. Pearson correlation analysis was used to identify immune-related lncRNAs. A competing endogenous RNA network was constructed and hub lncRNAs were identified. A total of 1650 coding genes, 821 lncRNAs and 62 miRNAs were commonly deregulated in the three datasets. A total of 408 lncRNAs associated with the overall survival of patients with ccRCC were identified. Among them, 82 lncRNAs were distant metastasis-related. Further analysis identified 52 lncRNAs associated with the immune pathway. Functional analyses concordantly demonstrated the role of the 52 lncRNAs in metastasis and tumor immunology. The ceRNA network analysis indicated lncRNA DSCR9 as the key lncRNA regulator. Univariate and multivariate analysis in two independent cohorts validated that DSCR9 could be an independent risk factor for the progression-free survival of patients with ccRCC. Further analyses indicated that DSCR9 might be associated with the immunotherapeutic response. reverse transcription-quantitative PCR demonstrated that the RNA expression level of DSCR9 was upregulated in ccRCC compared with normal kidney samples. The present study demonstrated the potential of LncRNA DSCR9 in assessing the prognosis and developing future immunotherapy for patients with metastatic ccRCC.

Identification and Experimental Validation of LINC00582 Associated with B Cell Immune and Development of Pulpitis: Bioinformatics and In Vitro Analysis.

Pulpitis is a common oral disease. Increasing evidence has demonstrated that long non-coding RNAs (lncRNAs) can regulate the immune response in pulpitis. This study focused on finding the key immune-related lncRNAs that regulate the development of pulpitis. Differentially expressed lncRNAs were analyzed. Enrichment analysis was performed to explore the function of differentially expressed genes. Immune cell infiltration was evaluated with Immune Cell Abundance Identifier. Cell Counting Kit-8 (CCK-8) and lactate dehydrogenase release assays were conducted to measure the viability of human dental pulp cells (HDPCs) and BALL-1 cells. Transwell assay was processed to prove migration and invasion of BALL-1 cells. Our results revealed that 17 lncRNAs were significantly upregulated. Pulpitis-related genes were mainly enriched in inflammatory relative signal pathways. The abundance of various immune cells was significantly abnormal in pulpitis tissues, among which the expression of eight lncRNAs was significantly correlated with the expression of B cell marker protein CD79B. As the most relevant lncRNA for B cells, LINC00582 could regulate the proliferation, migration, invasion, and CD79B expression of BALL-1 cells. Our study identified eight B cell immune-related lncRNAs. Meanwhile, LINC00582 has a positive effect on B cell immunity in the development of pulpitis.

Immune-Related Long Non-Coding RNA Signatures for Tongue Squamous Cell Carcinoma

Tongue squamous cell carcinoma (TSCC) represents one of the major subsets of head and neck cancer, which is characterized by unfavorable prognosis, frequent lymph node metastasis, and high mortality rate. The molecular events regulating tongue tumorigenesis remain elusive. In this study, we aimed to identify and evaluate immune-related long non-coding RNAs (lncRNAs) as prognostic biomarkers in TSCC. The lncRNA expression data for TSCC were obtained from The Cancer Genome Atlas (TCGA) and the immune-related genes were downloaded from the Immunology Database and Analysis Portal (ImmPort). Pearson correlation analysis was performed to identify immune-related lncRNAs. The TCGA TSCC patient cohort was randomly divided into training and testing cohorts. In the training cohort, univariate and multivariate Cox regression analyses were used to determining key immune-related lncRNAs, which were then validated through Cox regression analysis, principal component analysis (PCA), and receiver operating characteristic (ROC) analysis in the testing cohort. Six immune-related signature lncRNAs (MIR4713HG, AC104088.1, LINC00534, NAALADL2-AS2, AC083967.1, FNDC1-IT1) were found to have prognostic value in TSCC. Multivariate and univariate cox regression analyses showed that the risk score based on our six-lncRNA model, when compared to other clinicopathological factors (age, gender, stage, N, T), was an important indicator of survival rate. In addition, Kaplan-Meier survival analysis demonstrated significantly higher overall survival in the low-risk patient group than the high-risk patient group within both training and testing cohorts. The ROC analysis indicated that the AUCs for 5-year overall survival were 0.790, 0.691, and 0.721, respectively, for training, testing, and entire cohorts. Finally, PCA analysis demonstrated that the high-risk and low-risk patient groups presented significant deviation regarding their immune status. A prognostic model based on six immune-related signature lncRNAs was established. This six-lncRNA prognostic model has clinical significance and may be helpful in the development of personalized immunotherapy strategies.

Integrating multiple machine learning algorithms for prognostic prediction of gastric cancer based on immune-related lncRNAs.

Background: Long non-coding RNAs (lncRNAs) play an important role in the immune regulation of gastric cancer (GC). However, the clinical application value of immune-related lncRNAs has not been fully developed. It is of great significance to overcome the challenges of prognostic prediction and classification of gastric cancer patients based on the current study. Methods: In this study, the R package ImmLnc was used to obtain immune-related lncRNAs of The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) project, and univariate Cox regression analysis was performed to find prognostic immune-related lncRNAs. A total of 117 combinations based on 10 algorithms were integrated to determine the immune-related lncRNA prognostic model (ILPM). According to the ILPM, the least absolute shrinkage and selection operator (LASSO) regression was employed to find the major lncRNAs and develop the risk model. ssGSEA, CIBERSORT algorithm, the R package maftools, pRRophetic, and clusterProfiler were employed for measuring the proportion of immune cells among risk groups, genomic mutation difference, drug sensitivity analysis, and pathway enrichment score. Results: A total of 321 immune-related lncRNAs were found, and there were 26 prognostic immune-related lncRNAs. According to the ILPM, 18 of 26 lncRNAs were selected and the risk score (RS) developed by the 18-lncRNA signature had good strength in the TCGA training set and Gene Expression Omnibus (GEO) validation datasets. Patients were divided into high- and low-risk groups according to the median RS, and the low-risk group had a better prognosis, tumor immune microenvironment, and tumor signature enrichment score and a higher metabolism, frequency of genomic mutations, proportion of immune cell infiltration, and antitumor drug resistance. Furthermore, 86 differentially expressed genes (DEGs) between high- and low-risk groups were mainly enriched in immune-related pathways. Conclusion: The ILPM developed based on 26 prognostic immune-related lncRNAs can help in predicting the prognosis of patients suffering from gastric cancer. Precision medicine can be effectively carried out by dividing patients into high- and low-risk groups according to the RS.

Abstract 5703: The immune cell state atlas analysis predicts therapeutic benefits with immune checkpoint inhibitors

Abstract Introduction: In this study, we investigated the prognostic role of the immune cell state atlas in predicting therapeutic benefits of patients treated with immune checkpoint inhibitors (ICI) within the ORIEN network of 18 collaborating cancer centers under the Total Cancer Care protocol. Methods: We utilized RNA-seq data of 926 samples generated from 875 individuals. Gene expression data were deconvoluted for immune cell states using the Carcinoma EcoTyper software. We then conducted a series of survival analyses to test the association between survival outcomes and predicted cell types and states in five malignant tumors: Genitourinary (GU), Gastrointestinal (GI), Thoracic (THO), Cutaneous (CUT), Head & Neck (H&N). The regularized Cox regression model in R package ‘glmnet’ was then applied to select the complementary pathway signatures (including gene ontology and KEGG pathways) to the immune cell states in predicting survival outcomes. We also explored the immune-related long non-coding RNAs (lncRNA) as potential biomarkers for cell states and patient outcomes. Results: EcoTyper analysis revealed that 692 (~80%) of patients were assigned to the 10 pre-identified Carcinoma Ecotypes (CE1 to CE10) or cell state atlas group. Overall, two immune deficiency ecotype patient groups (CE1 and CE2) pre-identified based on the independent training data were linked to worse survival, while two proinflammatory ecotype groups (CE9 and CE10) were associated with favorable surxvival. Those ecotype groups showed strong prognostic significance in predicting OS in melanoma and H&N. Meanwhile, CE6, a non-neoplastic tissue enriched cell subtype, was also found to be highly associated with longer OS in H&N and GU. CE7, an age-related mutation patient subgroup, contributed to shorter survival in both melanoma and GI. We also found that a subset of activated B cell state and the exhausted/effector CD4 T cell state were significantly associated with patient survival in melanoma and GU, respectively. The penalized Cox regression model revealed that β-catenin signaling pathway, P53 pathway and heme metabolism in the MSigDB Hallmark gene sets are the most complementary pathways to the ecotype scores in multiple cancer types. In additional, multiple pathways in KEGG such as endocytosis were found to jointly contribute to the ecotype-pathway composite prognostic model. In anazlying immune-related lncRNA biomarkers, we highlighted the prognostic role of NKILA in our dataset, which has been studied to promote tumor immune evasion. Conclusion: Our analysis has successfully established the utility of immune cell state atlas in predicting therapeutic benefits with ICIs. We expect that the discovered complementary signatures in the cancer-cell compartment will also lead to a novel spectrum of tumor-based biomarkers to ICI. Citation Format: Tingyi Li, Vineeth Sukrithan, Aakrosh Ratan, Martin McCarter, John Carpten, Howard Colman, Alexandra P. Ikeguchi, Igor Puzanov, Susanne Arnold, Michelle Churchman, Patrick Hwu, Paulo C. Rodriguez, William S. Dalton, George J. Weiner, Ahmad Tarhini, Xuefeng Wang. The immune cell state atlas analysis predicts therapeutic benefits with immune checkpoint inhibitors. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5703.

Integrative learning in developing an immunologic lncRNA signature as a consensus risk-stratification tool for lung adenocarcinoma.

In the tumor immune microenvironment, the contribution of innate and adaptive immune cells to tumor progression has been consistently demonstrated. However, reliable prognostic biomarkers for lung adenocarcinoma (LUAD) have not yet been identified. We thus developed and validated an immunologic long noncoding RNA (lncRNA) signature (ILLS) to facilitate the classification of patients with high and low risk and provide potential "made-to-measure" treatment choices. The LUAD data sets were obtained and processed from public databases of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The abundance of immune infiltration and its related pathways were calculated through consensus clustering, weighted gene coexpression network analysis (WGCNA), and an integrated ImmLnc to identify immune-related lncRNAs and extract immune-related prognostic lncRNAs. Based on the integrative procedure, the best algorithm composition was least absolute shrinkage and selection operator (LASSO) and stepwise Cox regression in both directions to develop the ILLS in the TCGA-LUAD data set and validate the predictive power of 4 independent data sets, GSE31210, GSE37745, GSE30219, and GSE50081 through survival analysis, receiver operating characteristic (ROC) analysis, and multivariate Cox regression. The concordance index (C-index) analysis was transversely compared with 49 published signatures in the above 5 data sets to further confirm its stability and superiority. Finally, drug sensitivity analysis was conducted to explore potential therapeutic agents. Patients from the high-risk groups consistently had worse overall survival (OS) compared to the low-risk groups. ILLS proved to be an independent prognostic factor with favorable sensitivity and specificity. Among the 4 GEO data sets, compared to those reported in the other literature, ILLS maintained stable prediction ability and was more suitable as a consensus risk-stratification tool. However, The Cancer Immunome Atlas and IMvigor210 data sets demonstrated practical utility in recognizing target populations with effective immunotherapy, while the high-risk group exhibited potential targets for certain chemotherapy drugs, such as carmustine, etoposide, arsenic trioxide, and alectinib. ILLS demonstrated superior and stable prognostic prediction ability and thus has potential as a tool for assisting in risk classification and clinical decision-making in patients with LUAD.

Identification of biomarkers of hepatocellular carcinoma gene prognosis based on the immune-related lncRNA signature of transcriptome data

Long non-coding RNAs (lncRNAs) are well established to have an important role in cancer. The goal of this research was to investigate the prognostic usefulness of putative immune-related lncRNAs in hepatocellular carcinoma (HCC). The developed lncRNA signature was validated using 343 HCC patients from The Cancer Genome Atlas (TCGA) and 81 samples from Gene Expression Omnibus (GEO). Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) analysis were used to analyze immune-related lncRNAs for HCC prognosis. Patients in the low-risk group survived substantially longer than those in the high-risk group (P < 0.05). The discovered signal might be a useful prognostic factor for predicting patient survival. Overall survival predicted some clinical net improvements, according to the nomogram. Numerous enrichment approaches (including gene set enrichment analysis) were utilized to investigate the underlying mechanisms. Drug metabolism, mTOR, and p53 signaling pathways were associated with high-risk groups. When the expression of lncRNA PRRT3-AS1 was silenced in HepG2 cells, the proliferation, migration, and invasion abilities of HepG2 cells were decreased, and apoptosis was enhanced. In the supernatant from HepG2 cells with PRRT3-AS1 knockdown, the anti-inflammatory factors IL-10 and TGF-1 were induced, whereas the pro-inflammatory factors IL-1β, TNF-α, and IL-6 were reduced (P < 0.05). After PRRT3-AS1 knockdown, the protein expression of CD24, THY1, LYN, CD47, and TRAF2 in HepG2 cells was attenuated (P < 0.05). The discovery of five immune-related lncRNA signatures has significant therapeutic significance for predicting patient prognosis and directing personalized treatment for patients with HCC, which requires additional prospective confirmation.

A novel immune-related LncRNA prognostic signature for cutaneous melanoma

A novel immune-related LncRNA prognostic signature for cutaneous melanoma

A model based on immune-related lncRNA pairs and its potential prognostic value in immunotherapy for melanoma

A model based on long non-coding RNA (lncRNA) pairs independent of expression quantification was constructed to evaluate prognosis melanoma and response to immunotherapy in melanoma. RNA sequencing data and clinical information were retrieved and downloaded from The Cancer Genome Atlas and the Genotype-Tissue Expression databases. We identified differentially expressed immune-related lncRNAs (DEirlncRNAs), matched them, and used least absolute shrinkage and selection operator and Cox regression to construct predictive models. The optimal cutoff value of the model was determined using a receiver operating characteristic curve and used to categorize melanoma cases into high-risk and low-risk groups. The predictive efficacy of the model with respect to prognosis was compared with that of clinical data and ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data). Then, we analyzed the correlations of risk score with clinical characteristics, immune cell invasion, anti-tumor, and tumor-promoting activities. Differences in survival, degree of immune cell infiltration, and intensity of anti-tumor and tumor-promoting activities were also evaluated in the high- and low-risk groups. A model based on 21 DEirlncRNA pairs was established. Compared with ESTIMATE score and clinical data, this model could better predict outcomes of melanoma patients. Follow-up analysis of the model's effectiveness showed that patients in the high-risk group had poorer prognosis and were less likely to benefit from immunotherapy compared with those in the low-risk group. Moreover, there were differences in tumor-infiltrating immune cells between the high-risk and low-risk groups. By pairing the DEirlncRNA, we constructed a model to evaluate the prognosis of cutaneous melanoma independent of a specific level of lncRNA expression.

The Two Faces of Immune-Related lncRNAs in Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinoma (HNSCC) is a group of cancers originating from the mucosal epithelium in the oral cavity, larynx, oropharynx, nasopharynx, and hypopharynx. Molecular factors can be key in the diagnosis, prognosis, and treatment of HNSCC patients. Long non-coding RNAs (lncRNAs) are molecular regulators composed of 200 to 100,000 nucleotides that act on the modulation of genes that activate signaling pathways associated with oncogenic processes such as proliferation, migration, invasion, and metastasis in tumor cells. However, up until now, few studies have discussed the participation of lncRNAs in modeling the tumor microenvironment (TME) to generate a protumor or antitumor environment. Nevertheless, some immune-related lncRNAs have clinical relevance, since AL139158.2, AL031985.3, AC104794.2, AC099343.3, AL357519.1, SBDSP1, AS1AC108010.1, and TM4SF19-AS1 have been associated with overall survival (OS). MANCR is also related to poor OS and disease-specific survival. MiR31HG, TM4SF19-AS1, and LINC01123 are associated with poor prognosis. Meanwhile, LINC02195 and TRG-AS1 overexpression is associated with favorable prognosis. Moreover, ANRIL lncRNA induces resistance to cisplatin by inhibiting apoptosis. A superior understanding of the molecular mechanisms of lncRNAs that modify the characteristics of TME could contribute to increasing the efficacy of immunotherapy.

Development and verification of a 7-lncRNA prognostic model based on tumor immunity for patients with ovarian cancer

BackgroundBoth immune-reaction and lncRNAs play significant roles in the proliferation, invasion, and metastasis of ovarian cancer (OC). In this study, we aimed to construct an immune-related lncRNA risk model for patients with OC.MethodSingle sample GSEA (ssGSEA) algorithm was used to analyze the proportion of immune cells in The Cancer Genome Atlas (TCGA) and the hclust algorithm was used to conduct immune typing according to the proportion of immune cells for OC patients. The stromal and immune scores were computed utilizing the ESTIMATE algorithm. Weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) analyses were utilized to detect immune cluster-related lncRNAs. The least absolute shrinkage and selection operator (LASSO) regression was conducted for lncRNA selection. The selected lncRNAs were used to construct a prognosis-related risk model, which was then validated in Gene Expression Omnibus (GEO) database and in vitro validation.ResultsWe identify two subtypes based on the ssGSEA analysis, high immunity cluster (immunity_H) and low immunity cluster (immunity_L). The proportion of patients in immunity_H cluster was significantly higher than that in immunity_L cluster. The ESTIMATE related scores are relative high in immunity_H group. Through WGCNA and LASSO analyses, we identified 141 immune cluster-related lncRNAs and found that these genes were mainly enriched in autophagy. A signature consisting of 7 lncRNAs, including AL391832.3, LINC00892, LINC02207, LINC02416, PSMB8.AS1, AC078788.1 and AC104971.3, were selected as the basis for classifying patients into high- and low-risk groups. Survival analysis and area under the ROC curve (AUC) of the signature pointed out that this risk model had high accuracy in predicting the prognosis of patients with OC. We also conducted the drug sensitive prediction and found that rapamycin outperformed in patient with high risk score. In vitro experiments also confirmed our prediction.ConclusionsWe identified 7 immune-related prognostic lncRNAs that effectively predicted survival in OC patients. These findings may offer a valuable indicator for clinical stratification management and personalized therapeutic options for these patients.

Construction of A Novel Prognostic Model with Molecular Targets for Hepatocellular Carcinoma Based on Bioinformatics Applications

We aimed to analyze the differentially expressed genes associated with hepatocellular carcinoma (HCC) by bioinformatics method and to identify potential molecular targets for immunotherapy and molecular indicators for predicting HCC prognosis. Gene Expression Omnibus (GEO) was used to download the hepatocellular carcinoma related microarray data. The R language’s Limma tool was used to identify the genes with differential expression. For differentially expressed genes, GO (Gene Ontology) enrichment analysis, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis, and protein–protein interaction analysis were carried out. PPI (protein–protein interaction) regulation network construction. In order to further evaluate HCC specific differentially expressed genes, HCC specific expression analysis was carried out at the same time by merging with other tumour RNA-seq transcriptome data in the TCGA database. The relationship between immune-related LncRNA and independent risk factors was examined using univariate and multivariate Cox regression as well as Least absolute shrinkage and selection operator (LASSO) analysis. The proportional Hazards model (COX model) was utilized to model the chosen important genes and predict the prognosis. We obtained important genes through additional screening, and the GSE6764 validation set discovered that the expression of these genes decreased with increasing tumor stage (P &lt; 0.05). The prognosis analysis of the gene model revealed that the high-risk group had a dismal outcome. COX modelling was carried out for important genes. Meanwhile, the GSE76427 and GSE54236 validation sets validated the model’s survival analyses. By analyzing the gene expression profile of HCC utilizing cuttingedge bioinformatics techniques including Cox Regression and LASSO analysis, we were able to screen out the important modules and essential genes, create a predictive model for HCC, and propose possible biomarkers for the prediction of HCC.

Identification of Immune-Related Seven-Long Non-Coding RNA Signature for Overall Survival and Validation of the Effect of LINC01270 in Malignant Phenotypes of Clear Cell Renal Carcinoma.

The overall survival of patients with high-stage clear cell renal carcinoma (ccRCC) is poor. However, promising molecular-level prognostic marker is still lacking to date. We systematically evaluated the prognostic potential of immune-related long non-coding RNAs (lncRNAs) in ccRCC. The expressions of lncRNAs were validated in clinical tissues of ccRCC. Functional experiments were performed to investigate the role of lncRNAs in ccRCC. Eight hundred and ninety-three lncRNAs were differentially expressed in ccRCC and compared with normal controls and were screened out using three independent cohorts. Among them, 290 immune-related lncRNAs were identified. We identified a seven-lncRNA signature (LINC01270, FIRRE, RP11-37B2.1, RP11-253I19.3, RP11-438L19.1, RP11-504P24.9, and CTB-41I6.1) associated with the overall survival of late-stage ccRCC patients. Further multivariate Cox analysis using clinical factors as covariates showed that our lncRNA signature was an independent biomarker in training (P < 0.001, Log rank test) and validation cohorts (P = 0.003). The seven lncRNAs were closely related to the major targets (PD-1, PD-L1, and CTLA4) of immune checkpoint blockade drugs, implying that they may have potential value in predicting immunotherapy response. The seven lncRNAs may play an important role in tumor-infiltrating immune cells (eg, T/B cells) and tumor progression through regulating the binding of protein receptors/complexes, as revealed by functional analysis. qRT-PCR showed that LINC01270 was upregulated in ccRCC tissues (n=20) compared with paired normal samples. Functional experiments showed that LINC01270 silencing inhibited the proliferation, invasion, and migration of ccRCC cells. In summary, the seven-lncRNA signature has great potential in prognosis for patients with late-stage ccRCC, which could be a novel clinical biomarker. LINC01270 could be a novel therapeutic target of ccRCC.

Model construction and risk analysis of the lncRNA genes associated with the prognosis of esophageal adenocarcinoma with immune infiltration.

Our study analyzed the immune infiltration of esophageal adenocarcinoma (EAC) tumor cells and identified long non-coding ribonucleic acid (lncRNA) genes to construct a prognostic model of EAC to evaluate the survival prognosis of patients and explore potential therapeutic targets. The data of 89 patients with EAC, including 11 normal tissue samples and 78 EAC of tumor tissue samples, were downloaded from The Cancer Genome Atlas public database. Perl script and R software were used to run the code, conduct the statistical analysis, calculate the risk coefficients of the patients, and conduct the Cox regression analysis, immune-related lncRNA survival analysis, risk analysis, principal component analysis (PCA), and receiver operating characteristic (ROC) curve analysis. We screened and identified 19 prognostic biomarkers, including LINC01612, AC008443.2, and LINC02582, allocated the patients into high- and low-risk groups, and found significant differences in the prognosis between the high- and low-risk groups using the Kaplan-Meier survival analysis (P<0.001). A ROC curve was used to evaluate the feasibility of the prognostic model for EAC, and we found that the model had high predictability (area under the curve =0.964). A PCA analysis was performed of the complex transcriptome sequencing data and other cubes to transform the data into a 3-dimensional space constructed by feature vectors. Our study effectively screened and identified the lncRNA genes related to the immune infiltration of EAC and successfully constructed a prognostic model. In total, 19 potential diagnostic and therapeutic target genes, including LINC01612, AC008443.2, and LINC02582, were identified that have certain significance in guiding the clinical treatment of EAC patients.

Identification of prognostic immune-related lncRNA signature predicting the overall survival for colorectal cancer

Long non-coding RNA (lncRNA) is an important regulator of gene expression and serves a fundamental role in immune regulation. The present study aimed to develop a novel immune-related lncRNA signature to assess the prognosis of patients with colorectal cancer (CRC). Transcriptome data and clinical information of patients with CRC were downloaded from The Cancer Genome Atlas (TCGA) and UCSC Xena platforms. Immune-related mRNAs were extracted from the Molecular Signatures Database (MSigDB), and the immune-related lncRNAs were identified based on correlation analysis. Then, univariate, Lasso and multivariate Cox regression were applied to construct an immune-related lncRNA signature, and CRC patients were divided into high- and low-risk groups according to the median risk score. Finally, we evaluated the signature from the perspectives of clinical outcome, clinicopathological parameters, tumor-infiltrating immune cells (TIICs), immune status, tumor mutation burden (TMB) and immunotherapy responsiveness. In total, 272 immune-related lncRNAs were identified, five of which were applied to construct an immune-related lncRNA signature. The signature divided patients with CRC into low- and high-risk groups, the prognosis of patients in the high-risk group were significantly poorer than those in low-risk group, and the results were further confirmed in external validation cohort. Furthermore, the high-risk group showed aggressive clinicopathological characteristics, specific TIIC and immune function status, and low sensitivity to immunotherapy. The immune-related lncRNA signature could be exploited as a promising biomarker for predicting the prognosis and immune status of patients with CRC.

Comprehensive analysis of long noncoding RNAs and lncRNA-mRNA networks in snakehead (Channa argus) response to Nocardia seriolae infection

Evidence has been demonstrated that lncRNAs are involved in a variety of immune responses in vertebrate. It has been demonstrated that immune-related lncRNAs play vital functions in immune regulation against infections in teleost. Nocardia seriolae, as one of the Gram-positive bacteria, can cause chronic systemic granulomatous disease for snakehead (Channa argus). However, how lncRNAs function in the immune regulation process once snakehead was infected with N. seriolae infection has not been studied so far. Accordingly, transcription landscapes of lncRNAs and mRNAs in snakehead were investigated. A total of 1,991 lncRNA were obtained. Totally, we predicted 57,584 co-expression and 16,047 co-location lncRNA-mRNA pairs. To further analyze the potential function of these lncRNAs, GO enrichment analysis and KEGG signal pathways were performed on the target mRNAs of these differently expressed lncRNAs, suggesting that lncRNAs may play essential roles in modulating mRNA expression levels, and subsequently trigger downstream immune signaling pathways to regulate the immune response in snakehead. In addition, 9 DEmRNA and 3 lncRNAs were randomly selected for qRT-PCR analyzed, which confirmed the accuracy of transcriptome data. These results can provide novel knowledge about lncRNAs in immune responses process in snakehead, and can serve as important resources for further investigating the roles of lncRNAs during pathogen infections in teleost.

Comprehensive analysis of immune-related lncRNAs and their clinical relevance in gastric adenocarcinoma.

Increasing evidence has demonstrated that lncRNA plays a significant role in the immunity regulation of gastric adenocarcinoma. However, the immune-related lncRNAs and the prognostic value in immunotherapies remain largely unexplored. We collected immune-related lncRNA and the associated pathways of gastric cancer from the ImmLnc database. The cox regression model is used to analyze the prognostic value of these lncRNAs. Gastric cancer is further divided into different subtypes based on these lncRNAs. Tumor microenvironment analysis, functional enrichment analysis, and genomic alteration analysis are performed for different subtypes. Furthermore, chemotherapeutic and immunotherapeutic sensitivity are also analyzed among different subtypes. Nine lncRNAs are identified as significant regulators of the immune pathway of gastric cancer. Gastric cancer can be classified into 5 subtypes based on these lncRNAs. Tumor microenvironment analysis shows that cluster C3 has the highest immune score and C5 has the lowest score. Functional analysis shows that these subtypes are enriched with distinct biological processes. Genomic analysis shows that LAMA2 mutation is a protective factor in C3 but a risk factor in C5. Furthermore, these subtypes are found to respond distinctly to the same chemotherapeutic and immunotherapeutic drugs. In this study, we analyzed the immune-related lncRNA and identified the crucial role in the regulation of immune properties, biological processes, and immunotherapeutic sensitivity. These findings can improve our understanding of the epigenetic immunoregulation of lncRNA and advance the research of immunotherapy.