Immune-related lncRNA Signature Research Articles

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

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

A novel immune-related lncRNA signature predicts the prognosis and immune landscape in ccRCC.

As one of the most common tumors, the pathogenesis and progression of clear cell renal cell carcinoma (ccRCC) in the immune microenvironment are still unknown. The differentially expressed immune-related lncRNA (DEirlncRNA) was screened through co-expression analysis and the limma package of R, which based on the ccRCC project of the TCGA database. Then, we designed the risk model by irlncRNA pairs. In RCC patients, we have compared the area under the curve, calculated the Akaike Information Criterion (AIC) value of the 5-year receiver operating characteristic curve, determined the cut-off point, and established the optimal model for distinguishing the high-risk group from the low-risk group. We used the model for immune system assessment, immune point detection and drug sensitivity analysis after verifying the feasibility of the above model through clinical features. In our study, 1541 irlncRNAs were included. 739 irlncRNAs were identified as DEirlncRNAs to construct irlncRNA pairs. Then, 38 candidate DEirlncRNA pairs were included in the best risk assessment model through improved LASSO regression analysis. As a result, we found that in addition to age and gender, T stage, M stage, N stage, grade and clinical stage are significantly related to risk. Moreover, univariate and multivariate Cox regression analysis results reveals that in addition to gender, age, grade, clinical stage and risk score are independent prognostic factors. The results show that patients in the high-risk group are positively correlated with tumor infiltrating immune cells when the above model is applied to the immune system. But they are negatively correlated with endothelial cells, macrophages M2, mast cell activation, and neutrophils. In addition, the risk model was positively correlated with overexpressed genes (CTLA, LAG3 and SETD2, P<0.05). Finally, risk models can also play as an important role in predicting the sensitivity of targeted drugs. The new risk model may be a new method to predict the prognosis and immune status of ccRCC.

Prognostic Value of an Immune Long Non-Coding RNA Signature in Liver Hepatocellular Carcinoma.

In recent years, there has been a growing recognition of the important role that long non-coding RNAs (lncRNAs) play in the immunological process of hepatocellular carcinoma (LIHC). An increasing number of studies have shown that certain lncRNAs hold great potential as viable options for diagnosis and treatment in clinical practice. The primary objective of our investigation was to devise an immune lncRNA profile to explore the significance of immune-associated lncRNAs in the accurate diagnosis and prognosis of LIHC. Gene expression profiles of LIHC samples obtained from TCGA database were screened for immune-related genes. The optimal immune-related lncRNA signature was built via correlational analysis, univariate and multivariate Cox analysis. Then, the Kaplan-Meier plot, ROC curve, clinical analysis, gene set enrichment analysis, and principal component analysis were performed to evaluate the capability of the immune lncRNA signature as a prognostic indicator. Six long non-coding RNAs were identified via correlation analysis and Cox regression analysis considering their interactions with immune genes. Subsequently, tumor samples were categorized into two distinct risk groups based on different clinical outcomes. Stratification analysis indicated that the prognostic ability of this signature acted as an independent factor. The Kaplan-Meier method was employed to conduct survival analysis, results showed a significant difference between the two risk groups. The predictive performance of this signature was validated by principal component analysis (PCA). Additionally, data obtained from gene set enrichment analysis (GSEA) revealed several potential biological processes in which these biomarkers may be involved. To summarize, this study demonstrated that this six-lncRNA signature could be identified as a potential factor that can independently predict the prognosis of LIHC patients.

Immune-related lncRNAs signature and radiomics signature predict the prognosis and immune microenvironment of glioblastoma multiforme

BackgroundGlioblastoma multiforme (GBM) is the most common primary malignant brain tumor in adults. This study aimed to construct immune-related long non-coding RNAs (lncRNAs) signature and radiomics signature to probe the prognosis and immune infiltration of GBM patients.MethodsWe downloaded GBM RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) project database, and MRI data were obtained from The Cancer Imaging Archive (TCIA). Then, we conducted a cox regression analysis to establish the immune-related lncRNAs signature and radiomics signature. Afterward, we employed a gene set enrichment analysis (GSEA) to explore the biological processes and pathways. Besides, we used CIBERSORT to estimate the abundance of tumor-infiltrating immune cells (TIICs). Furthermore, we investigated the relationship between the immune-related lncRNAs signature, radiomics signature and immune checkpoint genes. Finally, we constructed a multifactors prognostic model and compared it with the clinical prognostic model.ResultsWe identified four immune-related lncRNAs and two radiomics features, which show the ability to stratify patients into high-risk and low-risk groups with significantly different survival rates. The risk score curves and Kaplan–Meier curves confirmed that the immune-related lncRNAs signature and radiomics signature were a novel independent prognostic factor in GBM patients. The GSEA suggested that the immune-related lncRNAs signature were involved in L1 cell adhesion molecular (L1CAM) interactions and the radiomics signature were involved signaling by Robo receptors. Besides, the two signatures was associated with the infiltration of immune cells. Furthermore, they were linked with the expression of critical immune genes and could predict immunotherapy’s clinical response. Finally, the area under the curve (AUC) (0.890,0.887) and C-index (0.737,0.817) of the multifactors prognostic model were greater than those of the clinical prognostic model in both the training and validation sets, indicated significantly improved discrimination.ConclusionsWe identified the immune-related lncRNAs signature and tradiomics signature that can predict the outcomes, immune cell infiltration, and immunotherapy response in patients with GBM.

A methylation- and immune-related lncRNA signature to predict ovarian cancer outcome and uncover mechanisms of chemoresistance

Tumor-associated lncRNAs regulated by epigenetic modification switches mediate immune escape and chemoresistance in ovarian cancer (OC). However, the underlying mechanisms and concrete targets have not been systematically elucidated. Here, we discovered that methylation modifications played a significant role in regulating immune cell infiltration and sensitizing OC to chemotherapy by modulating immune-related lncRNAs (irlncRNAs), which represent tumor immune status. Through deep analysis of the TCGA database, a prognostic risk model incorporating four methylation-related lncRNAs (mrlncRNAs) and irlncRNAs was constructed. Twenty-one mrlncRNA/irlncRNA pairs were identified that were significantly related to the overall survival (OS) of OC patients. Subsequently, we selected four lncRNAs to construct a risk signature predictive of OS and indicative of OC immune infiltration, and verified the robustness of the risk signature in an internal validation set. The risk score was an independent prognostic factor for OC prognosis, which was demonstrated via multifactorial Cox regression analysis and nomogram. Moreover, risk scores were negatively related to the expression of CD274, CTLA4, ICOS, LAG3, PDCD1, and PDCD1LG2 and negatively correlated with CD8+, CD4+, and Treg tumor-infiltrating immune cells. In addition, a high-risk score was associated with a higher IC50 value for cisplatin, which was associated with a significantly worse clinical outcome. Next, a competing endogenous RNA (ceRNA) network and a signaling pathway controlling the infiltration of CD8+ T cells were explored based on the lncRNA model, which suggested a potential therapeutic target for immunotherapy. Overall, this study constructed a prognostic model by pairing mrlncRNAs and irlncRNAs and revealed the critical role of the FTO/RP5-991G20.1/hsa-miR-1976/MEIS1 signaling pathway in regulating immune function and enhancing anticancer therapy.

Multi-center validation of an immune-related lncRNA signature for predicting survival and immune status of patients with renal cell carcinoma: an integrating machine learning-derived study.

Long noncoding RNAs (lncRNAs) have been reported to play an important role in tumor immune modification. Nonetheless, the clinical implication of immune-associated lncRNAs in renal cell carcinoma (RCC) remains to be further explored. 76 combinations of machine learning algorithms were integrated to develop and validate a machine learning-derived immune-related lncRNA signature (MDILS) in five independent cohorts (n = 801). We collected 28 published signatures and collated clinical variables for comparison with MDILS to verify its efficacy. Subsequently, molecular mechanisms, immune status, mutation landscape, and pharmacological profile were further investigated in different stratified patients. Patients with high MDILS displayed worse overall survival than those with low MDILS. The MDILS could independently predict overall survival and convey robust performance across five cohorts. MDILS has a significantly better performance compared with traditional clinical variables and 28 published signatures. Patients with low MDILS exhibited more abundant immune infiltration and higher potency of immunotherapeutic response, while patients with high MDILS might be more sensitive to multiple chemotherapeutic drugs (e.g., sunitinib and axitinib). MDILS is a robust and promising tool to facilitate clinical decision-making and precision treatment of RCC.

Exploration of Identification and Prognostic Analysis of a Novel Immune-Related lncRNA Pair Signature and Immune Landscape in Esophageal Adenocarcinoma: A New Method Based on “Continuous Learning” Model

With the rapid development of information technology, many medical systems have emerged one after another with the support of continuous learning. A method of medical data privacy protection and resource utilization based on continuous learning is proposed to initialize the depth model of specific medical tasks. The depth model includes feature sampling model, data review model and task expression model, Finally, the depth model is trained according to the data from n institutions in turn. This method can overcome the obstacles of data sharing. The intelligent medical system of medical knowledge sharing will greatly improve the level of existing medical technology. An increasing body of evidence suggests that long non-coding RNAs (lncRNAs) participate in various physiological processes and pathological diseases. Esophageal adenocarcinoma develops rapidly with poor prognosis and high mortality in the near and long term. Immunotargeted therapy is a research hotspot. However, it is necessary to explore the immunomodulatory molecules of esophageal adenocarcinoma and analyze their relationship with clinicopathological characteristics and prognosis. We aimed to construct a robust immune-related lncRNA signature associated with survival outcomes in esophageal adenocarcinoma. We identified an immune-related lncRNA pairs signature with prognostic value from The Cancer Genome Atlas. Differentially expressed immune-related lncRNAs (DEirlncRNAs) were identified and paired, followed by prognostic assessment using univariate Cox regression analysis. We used least absolute shrinkage and selection operator penalized Cox analysis for constructing a risk score prognostic model and drew receiver operating characteristic (ROC) curves to predict overall survival. Then, we evaluated our signature in several settings: chemotherapy, tumor-infiltrating immune cells, and immune-mediated gene expression. In total, 339 DEirlncRNA pairs were identified, 11 of which were involved in the risk score prognostic signature. The area under ROC curves representing the predictive effect for 1-, 2-, and 3-year survival rates were 0.942, 0.987, and 0.977, respectively. The risk score model was confirmed as an independent prognostic factor and was significantly superior to clinicopathological characteristics. Correlation analyses showed disparities in drug sensitivity, tumor-infiltrating immune cells, and immune-related gene expression. We identified a novel prognostic immune-related lncRNA pair signature for esophageal adenocarcinoma. The risk score-based groups displayed different immune statuses, drug sensitivity, and immune-mediated gene expression. These findings may offer insights into the prognostic evaluation of esophageal adenocarcinoma and may provide a basis for creating personalized treatment plans.

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.

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.

Construction and Validation of Immune-related LncRNAs Signature to Predict the Prognosis and Therapeutic Efficacy of Breast Cancer

Construction and Validation of Immune-related LncRNAs Signature to Predict the Prognosis and Therapeutic Efficacy of Breast Cancer

Identification and Application of a Novel Immune-Related lncRNA Signature on the Prognosis and Immunotherapy for Lung Adenocarcinoma.

Long non-coding RNA (lncRNA) participates in the immune regulation of lung cancer. However, limited studies showed the potential roles of immune-related lncRNAs (IRLs) in predicting survival and immunotherapy response of lung adenocarcinoma (LUAD). Based on The Cancer Genome Atlas (TCGA) and ImmLnc databases, IRLs were identified through weighted gene coexpression network analysis (WGCNA), Cox regression, and Lasso regression analyses. The predictive ability was validated by Kaplan-Meier (KM) and receiver operating characteristic (ROC) curves in the internal dataset, external dataset, and clinical study. The immunophenoscore (IPS)-PD1/PD-L1 blocker and IPS-CTLA4 blocker data of LUAD were obtained in TCIA to predict the response to immune checkpoint inhibitors (ICIs). The expression levels of immune checkpoint molecules and markers for hyperprogressive disease were analyzed. A six-IRL signature was identified, and patients were stratified into high- and low-risk groups. The low-risk had improved survival outcome (p = 0.006 in the training dataset, p = 0.010 in the testing dataset, p &lt; 0.001 in the entire dataset), a stronger response to ICI (p &lt; 0.001 in response to anti-PD-1/PD-L1, p &lt; 0.001 in response to anti-CTLA4), and higher expression levels of immune checkpoint molecules (p &lt; 0.001 in PD-1, p &lt; 0.001 in PD-L1, p &lt; 0.001 in CTLA4) but expressed more biomarkers of hyperprogression in immunotherapy (p = 0.002 in MDM2, p &lt; 0.001 in MDM4). The six-IRL signature exhibits a promising prediction value of clinical prognosis and ICI efficacy in LUAD. Patients with low risk might gain benefits from ICI, although some have a risk of hyperprogressive disease.

Identification and validation of an immune-related lncRNAs signature to predict the overall survival of ovarian cancer.

Ovarian cancer (OC) is the most lethal gynecological cancer in women. Studies had reported that immune-related lncRNAs signatures were valuable in predicting the survival and prognosis of patients with various cancers. In our study, the prognostic value of immune-related lncRNAs was investigated in OC patients from TCGA-RNA-seq cohort (n=378) and HG-U133_Plus_2 cohort (n=590), respectively. Pearson correlation analysis was implemented to screen the immune-related lncRNA and then univariate Cox regression analysis was performed to explore their prognostic value in OC patients. Five prognostic immune-related lncRNAs were identified as prognostic lncRNAs. Besides, they were inputted into a LASSO Cox regression to establish and validate an immune-related lncRNA prognostic signature in TCGA-RNA-Seq cohort and HG-U133_Plus_2 cohort, respectively. Based on the best cut-off value of risk score, patients were divided into high- and low-risk groups. Survival analysis suggested that patients in the high-risk group had a worse overall survival (OS) than those in the low-risk group in both cohorts. The association between clinicopathological feathers and risk score was then evaluated by using stratification analysis. Moreover, we constructed a nomogram based on risk score, age and stage, which had a strong ability to forecast the OS of the OC patients. The influence of risk score on immune infiltration and immunotherapy response were assessed and the results suggested that patients with high-risk score might recruit multiple immune cells and stromal cells, leading to facilitating immune surveillance evasive. Ultimately, we demonstrated that the risk model was associated with chemotherapy response of multiple antitumor drugs, especially for paclitaxel, metformin and veliparib, which are commonly used in treating OC patients. In conclusion, we constructed a novel immune-related lncRNA signature, which had a potential prognostic value for OC patients and might facilitate personalized counselling for immunotherapy and chemotherapy.

A novel signature to predict thyroid cancer prognosis and immune landscape using immune-related LncRNA pairs

BackgroundThyroid cancer (TC) is the most common endocrine malignancy worldwide. The incidence of TC is high and increasing worldwide due to continuous improvements in diagnostic technology. Therefore, identifying accurate prognostic predictions to stratify TC patients is important.MethodsRaw data were downloaded from the TCGA database, and pairwise comparisons were applied to identify differentially expressed immune-related lncRNA (DEirlncRNA) pairs. Then, we used univariate Cox regression analysis and a modified Lasso algorithm on these pairs to construct a risk assessment model for TC. We further used qRT‒PCR analysis to validate the expression levels of irlncRNAs in the model. Next, TC patients were assigned to high- and low-risk groups based on the optimal cutoff score of the model for the 1-year ROC curve. We evaluated the signature in terms of prognostic independence, predictive value, immune cell infiltration, immune status, ICI-related molecules, and small-molecule inhibitor efficacy.ResultsWe identified 14 DEirlncRNA pairs as the novel predictive signature. In addition, the qRT‒PCR results were consistent with the bioinformatics results obtained from the TCGA dataset. The high-risk group had a significantly poorer prognosis than the low-risk group. Cox regression analysis revealed that this immune-related signature could predict prognosis independently and reliably for TC. With the CIBERSORT algorithm, we found an association between the signature and immune cell infiltration. Additionally, immune status was significantly higher in low-risk groups. Several immune checkpoint inhibitor (ICI)-related molecules, such as PD-1 and PD-L1, showed a negative correlation with the high-risk group. We further discovered that our new signature was correlated with the clinical response to small-molecule inhibitors, such as sunitinib.ConclusionsWe have constructed a prognostic immune-related lncRNA signature that can predict TC patient survival without considering the technical bias of different platforms, and this signature also sheds light on TC’s overall prognosis and novel clinical treatments, such as ICB therapy and small molecular inhibitors.

Machine Learning-Devised Immune-Related lncRNA Signature Panel Predicts the Prognosis and Immune Landscape in Breast Cancer Novel IRLP Signature in BRCA.

Long noncoding RNAs (lncRNAs) actively participate in breast cancer (BRCA) tumorigenesis via epigenetic mechanisms. Our study identified immune-related lncRNA (irlncRNA) pairs and compiled them into a set of noncoding gene signatures able to stratify subtypes of BRCA associated with variable degrees of survival and immune cell infiltration. A 40 immune-related lncRNA pair (IRLP) signature including 43 irlncRNAs was built, with high sensitivity and specificity for the prediction of survival in different molecular subtypes of BRCA. Results demonstrated that the low-risk group showed a significantly longer survival rate, and this novel IRLP signature was highly associated with survival status, T stage, metastatic disease, and overall stage in BRCA. Immune infiltrating analyses found that the low-risk group has a lower expression level of macrophage M2 and a higher expression level of immunosuppressed biomarkers than the high-risk group. DEirlncRNAs were further proven to be significantly related to the MAPK signaling, Jak-STAT signaling, and ErbB signaling pathways in BRCA. In conclusion, the 40 IRLP signature showed a promising clinical prediction value in the prognosis of different molecular subtypes and immunotherapy response in BRCA, and the underlying mechanism for these IRLPs warrants further investigations.

Computational identification of immune-related lncRNA signature for predicting the prognosis and immune landscape of human glioblastoma multiforme.

Emerging evidence shows immune-related long noncoding RNAs (ir-lncRNAs) perform critical roles in tumor progression and prognosis assessment. However, the identification of ir-lncRNAs and their clinical significance in human glioblastoma multiforme (GBM) remain largely unexplored. Here, a designed computational frame based on immune score was used to identify differentially expressed ir-lncRNAs (DEir-lncRNAs) from The Cancer Genome Atlas (TCGA) GBM program. The immune-related lncRNA signature (IRLncSig) composed of prognosis-related DEir-lncRNAs selected by Cox regression analysis and its clinical predictive values were verified, which was further validated by another dataset from the Gene Expression Omnibus database (GEO). Subsequently, the association between IRLncSig and immune cell infiltration, immune checkpoint inhibitor (ICI) biomarkers, O6-methylguanine-DNA methyltransferase (MGMT) gene expression, and biological function were also analyzed. After calculation, five prognosis-related ir-lncRNAs were included in the establishment of IRLncSig. The risk assessment based on IRLncSig indicated that the high-IRLncSig-score group was significantly associated with poor prognosis (p < 0.001), significant aggregation of macrophages (p < 0.05), higher ICI biomarker expression, and MGMT gene expression (p < 0.05). Signature-related lncRNAs may be involved in immune activities in the tumorigenesis and progression of GBM. In summary, the novel IRLncSig shows a promising clinical value in predicting the prognosis and immune landscape of GBM.

A robust immune-related lncRNA signature for the prognosis of human colorectal cancer.

Background: Colorectal cancer (CRC) is one of the most prevalent malignant cancers worldwide. Immune-related long non-coding RNAs (IRlncRNAs) are proved to be essential in the development and progression of carcinoma. The purpose of the present study was to develop and validate a prognostic IRlncRNA signature for CRC patients.Methods: Gene expression profiles of CRC samples were downloaded from The Cancer Genome Atlas (TCGA) database. Immune-related genes were obtained from the ImmPort database and were used to identify IRlncRNA by correlation analysis. Through LASSO Cox regression analyses, a prognostic signature was constructed. Functional enrichment analysis was performed by gene set enrichment analysis (GSEA). TIMER2.0 web server and tumor immune dysfunction and exclusion (TIDE) algorithm were employed to analyze the association between our model and tumor-infiltrating immune cells and immunotherapy response. The expression levels of IRlncRNAs in cell lines were detected by quantitative real-time PCR (qPCR).Results: A 9-IRlncRNA signature was developed by a LASSO Cox proportional regression model. Based on the signature, CRC patients were divided into high- and low-risk groups with different prognoses. GSEA results indicated that patients in high-risk group were associated with cancer-related pathways. In addition, patients in low-risk group were found to have more infiltration of anti-tumor immune cells and might show a favorable response to immunotherapy. Finally, the result of qPCR revealed that most IRlncRNAs were differently expressed between normal and tumor cell lines.Conclusion: The constructed 9-IRlncRNA signature has potential to predict the prognosis of CRC patients and may be helpful to guide personalized immunotherapy.

Systematic investigation of immune-related lncRNA landscape reveals a potential long non-coding RNA signature for predicting prognosis in kidney renal clear cell carcinoma.

e16510 Background: Increasing evidence has revealed that long non-coding RNAs (lncRNAs) play a crucial role in cancer immunity. However, the comprehensive landscape of immune infiltration-associated lncRNAs and their potential roles in the prognosis and diagnosis of kidney renal clear cell carcinoma (KIRC) remains largely unexplored. Methods: The transcriptomics data, expression profiles, and clinical information of KIRC patients were obtained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database. Novel lncRNAs were identified by a custom pipeline. The infiltration of immune cells was calculated by ssGSEA. K-means consensus method was used to cluster KIRC samples in different groups. Immune-related lncRNAs were obtained by differential expression analysis based on these groups. Univariate Cox regression, LASSO regression, and multivariate Cox regression analysis were performed on GEO datasets to construct an immune-related lncRNAs signature for predicting the prognosis of KIRC. And then, validated the prognostic signature in TCGA data. Results: Based on 261 samples from KIRC patients, tens of thousands of novel lncRNA genes were identified. KIRC samples were clustered into three immune clusters based on the infiltration of immune cells. Based on these groups, 241 immune-related lncRNAs were obtained. Moreover, a large part of immune-related lncRNAs could be detected in urinary samples of KIRC. Through a series of analyses, three immune-related lncRNAs were identified as a prognostic signature for KIRC. KIRC patients in the high-risk group had a shorter OS than those in the low-risk group both in the training set (P &lt; 0.001) and TCGA data (P &lt; 0.001), respectively. The AUC was 0.9 in the training set. Univariate and multivariate Cox regression analysis confirmed that the immune-related lncRNAs signature could be an independent prognostic factor both in the training set and TCGA data. Conclusions: All these findings provide a more comprehensive lncRNAs catalog for investigating the lncRNAs and provide a novel approach for the effective prediction of clinical outcomes in KIRC.

Construction of an immune-related lncRNA signature pair for predicting oncologic outcomes and the sensitivity of immunosuppressor in treatment of lung adenocarcinoma

BackgroundAlthough immunotherapy has shown clinical activity in lung adenocarcinoma (LUAD), LUAD prognosis has been a perplexing problem. We aimed to construct an immune-related lncRNA pairs (IRLPs) score for LUAD and identify what immunosuppressor are appropriate for which group of people with LUAD.MethodsBased on The Cancer Genome Atlas (TCGA)-LUAD cohort, IRLPs were identified to construct an IRLPs scoring system by Cox regression and validated in the Gene Expression Omnibus (GEO) dataset using log-rank test and the receiver operating characteristic curve (ROC). Next, we used spearman’s correlation analysis, t-test, signaling pathways analysis and gene mutation analysis to explore immune and molecular characteristics in different IRLP subgroups. The “pRRophetic” package was used to predict the sensitivity of immunosuppressant.ResultsThe IRLPs score was constructed based on eight IRLPs calculated as 2.12 × (MIR31HG|RRN3P2) + 0.43 × (NKX2-1-AS1|AC083949.1) + 1.79 × (TMPO-AS1|LPP-AS2) + 1.60 × (TMPO-AS1|MGC32805) + 1.79 × (TMPO-AS1|PINK1-AS) + 0.65 × (SH3BP5-AS1|LINC01137) + 0.51 × (LINC01004|SH3PXD2A-AS1) + 0.62 × (LINC00339|AGAP2-AS1). Patients with a lower IRLPs risk score had a better overall survival (OS) (Log-rank test PTCGA train dataset < 0.001, PTCGA test dataset = 0.017, PGEO dataset = 0.027) and similar results were observed in the AUCs of TCGA dataset and GEO dataset (AUC TCGA train dataset = 0.777, AUC TCGA test dataset = 0.685, AUC TCGA total dataset = 0.733, AUC GEO dataset = 0.680). Immune score (Cor = -0.18893, P < 0.001), stoma score (Cor = -0.24804, P < 0.001), and microenvironment score (Cor = -0.22338, P < 0.001) were significantly decreased in the patients with the higher IRLP risk score. The gene set enrichment analysis found that high-risk group enriched in molecular changes in DNA and chromosomes signaling pathways, and in this group the tumor mutation burden (TMB) was higher than in the low-risk group (P = 0.0015). Immunosuppressor methotrexate sensitivity was higher in the high-risk group (P = 0.0052), whereas parthenolide (P < 0.001) and rapamycin (P = 0.013) sensitivity were lower in the high-risk group.ConclusionsOur study established an IRLPs scoring system as a biomarker to help in the prognosis, the identification of molecular and immune characteristics, and the patient-tailored selection of the most suitable immunosuppressor for LUAD therapy.

Development of six immune-related lncRNA signature prognostic model for smoking-positive lung adenocarcinoma.

BackgroundSmoking is one of the most hazardous risk factors for the development of lung adenocarcinoma (LUAD). Many survival and prognosis‐related biomarkers were discovered using database mining. However, the precision of immune‐related long noncoding RNAs (lncRNAs) predictions is insufficient. We identified a novel signature to improve the estimate of smoking‐related LUAD prognosis.MethodsThe Cancer Genome Atlas database (TCGA) was used to obtain the LUAD lncRNA expression profiles. The smoking‐related LUAD cohort was randomly split into discovery and validation cohorts. To determine the risk score, use the LASSO Cox regression technique on the prognostic immune‐related lncRNA. The risk signature has been developed.ResultsA total of 643 immune‐related lncRNAs were identified as potential candidates for a risk signature. Finally, six immune‐related lncRNAs (AL359915.2, AP000695.1, HSPC324, TGFB2‐AS1, AC026355.1, and AC002128.2) were identified and used to carry out risk signature, which showed a close association with overall survival in the discovery cohort. We classified patients as high risk or low risk based on a median risk score of 1.0783. In the discovery cohort, overall survival was marginally longer in the low‐risk group than in the high‐risk category (p = 2.28e08). The area under the curves (AUC) for 1‐, 3‐, and 5‐year survival was 0.67, 0.7, and 0.82, respectively. Furthermore, we successfully validated and combined cohorts using this risk profile. We discovered a strong positive connection between HSPC324 and VIPR1 as a possible novel biomarker for smoking‐related LUAD development in our study.ConclusionsOur research has established a six immune‐lncRNA signature that may be used to predict the prognosis of smoking‐related LUAD with great accuracy.

Construction of an Immune-Related lncRNA Signature That Predicts Prognosis and Immune Microenvironment in Osteosarcoma Patients.

Osteosarcoma is one of the most common bone tumors in teenagers. We hope to provide a reliable method to predict the prognosis of osteosarcoma and find potential targets for early diagnosis and precise treatment. To address this issue, we performed a detailed bioinformatics analysis based on the Cancer Genome Atlas (TCGA). A total of 85 osteosarcoma patients with gene expression data and clinicopathological features were included in this study, which was considered the entire set. They were randomly divided into a train set and a test set. We identified six lncRNAs (ELFN1-AS1, LINC00837, OLMALINC, AL669970.3, AC005332.4 and AC023157.3), and constructed a signature that exhibited good predictive ability of patient survival and metastasis. What’s more, we found that risk score calculated by the signature was positively correlated to tumor purity, CD4+ naive T cells, and negatively correlated to CD8+ T cells. Furthermore, we investigated each lncRNA in the signature and found that these six lncRNAs were associated with tumorigenesis and immune cells in the tumor microenvironment. In conclusion, we constructed and validated a signature, which had good performance in the prediction of survival, metastasis and immune microenvironment. Our study indicated possible mechanisms of these lncRNAs in the development of osteosarcoma, which may provide new insights into the precise treatment of osteosarcoma.