Non-coding RNA Signature Research Articles

Urgent need for prognostic markers for hepatocellular carcinoma in the light of genomic instability and non-coding RNA signatures

Urgent need for prognostic markers for hepatocellular carcinoma in the light of genomic instability and non-coding RNA signatures

Identification of a pyroptosis-related long noncoding RNA signature for determining the prognosis and immune status of hepatocellular carcinoma patients.

Despite improvements in cancer screening and diagnosis, hepatocellular carcinoma (HCC) is still diagnosed at an advanced stage, and the prognosis is worse than that of early HCC patients. Therefore, better molecular markers and therapeutic targets in HCC are required. We investigated the predictive value of pyroptosis-related long noncoding RNAs (lncRNAs) in HCC and the effects of these lncRNAs on the immune microenvironment of HCC. RNA sequencing data of HCC patients were extracted from The Cancer Genome Atlas (TCGA) database to identify differentially expressed pyroptosis-related lncRNAs related to overall survival (OS). A model was established to verify the character of pyroptosis-associated lncRNAs in the tumor microenvironment, and their prognostic value was evaluated. A total of 721 PR lncRNAs were identified based on the analysis of the TCGA database. Univariate Cox analysis revealed 37 survival-related PRlncRNAs with prognostic values. As a result of least absolute shrinkage and selection operator (LASSO) regression analysis, 'ELFN-AS1', AC099850.3, AC073389.3, 'HPN-AS1', AC009283.1, and AL139289.1 showed prognostic value. Kaplan-Meier analysis indicated that the OS of the high-risk set was worse than those of the low-risk set in both the training and testing cohorts. Univariate and multivariate analyses revealed that the risk score was a better independent prognostic factor than the stage. The precision of the lncRNA signature was confirmed using receiver operating characteristic curve (ROC) analysis. Immuneand metabolism-related pathways were enriched in both the lowand high-risk groups. Gene set enrichment analysis suggested that the identified lncRNAs regulate HCC tumorigenesis and prognosis by modulating metabolism. Various algorithms were used to confirm the significant differences in immune cells between these 2 groups. These findings could contribute to the development and validation of favorable biomarkers, improve the prognosis and survival of HCC, and help in developed individualized treatment plans for HCC.

Identification and experimental validation of a sialylation–related long noncoding RNA signature for prognosis of bladder cancer

BackgroundThe dysregulation of sialylation plays a pivotal role in cancer progression and metastasis, impacting various aspects of tumor behavior. This study aimed to investigate the prognostic significance of long non-coding RNAs (lncRNAs) in relation to sialylation. Additionally, we aimed to develop a signature of sialylation-related lncRNAs in the context of bladder cancer.MethodsThis study used transcriptomic data and clinical information from the TCGA (the Cancer Genome Atlas) database to screen for sialylation-related lncRNAs and constructed a prognostic model. The relationships between these lncRNAs and biological pathways, immune cell infiltration, drug sensitivity, etc., were analyzed, and the expression of some lncRNAs was validated at the cellular level.ResultsThis study identified 6 prognostic lncRNAs related to sialylation and constructed a risk score model with high predictive accuracy and reliability. The survival period of patients in the high-risk group was significantly lower than that of the low-risk group, and it was related to various biological pathways and immune functions. In addition, this study found differences in the sensitivity of patients in different risk groups to chemotherapy drugs, providing a reference for personalized treatment.ConclusionIn this study, we examined the relationship between sialylation-related lncRNA and the prognosis of bladder cancer, providing new molecular markers and potential targets for diagnosis and treatment. Our research revealed correlations between sialylation-related lncRNA characteristics and clinicopathological features, potential mechanisms, somatic mutations, immune microenvironment, chemotherapy response, and predicted drug sensitivity in bladder cancer. Additionally, in vitro cellular studies were conducted to validate these findings and lay the groundwork for future clinical applications.

EP.06G.14 Discovery of Immune-Related Long Noncoding RNA Signatures in Lung Adenocarcinoma

EP.06G.14 Discovery of Immune-Related Long Noncoding RNA Signatures in Lung Adenocarcinoma

As a novel prognostic model for breast cancer, the identification and validation of telomere-related long noncoding RNA signatures

BackgroundTelomeres are a critical component of chromosome integrity and are essential to the development of cancer and cellular senescence. The regulation of breast cancer by telomere-associated lncRNAs is not fully known, though. The goals of this study were to describe predictive telomere-related LncRNAs (TRL) in breast cancer and look into any possible biological roles for these RNAs.MethodsWe obtained RNA-seq data, pertinent clinical data, and a list of telomere-associated genes from the cancer genome atlas and telomere gene database, respectively. We subjected differentially expressed TRLs to co-expression analysis and univariate Cox analysis to identify a prognostic TRL. Using LASSO regression analysis, we built a prognostic model with 14 TRLs. The accuracy of the model’s prognostic predictions was evaluated through the utilization of Kaplan-Meier (K-M) analysis as well as receiver operating characteristic (ROC) curve analysis. Additionally, immunological infiltration and immune drug prediction were done using this model. Patients with breast cancer were divided into two subgroups using cluster analysis, with the latter analyzed further for variations in response to immunotherapy, immune infiltration, and overall survival, and finally, the expression of 14-LncRNAs was validated by RT-PCR.ResultsWe developed a risk model for the 14-TRL, and we used ROC curves to demonstrate how accurate the model is. The model may be a standalone prognostic predictor for patients with breast cancer, according to COX regression analysis. The immune infiltration and immunotherapy results indicated that the high-risk group had a low level of PD-1 sensitivity and a high number of macrophages infiltrating. In addition, we’ve discovered a number of small-molecule medicines with considerable for use in treating high-risk groups. The cluster 2 subtype showed the highest immune infiltration, the highest immune checkpoint expression, and the worst prognosis among the two subtypes defined by cluster analysis, which requires more attention and treatment.ConclusionAs a possible biomarker, the proposed 14-TRL signature could be utilized to evaluate clinical outcomes and treatment efficacy in breast cancer patients.

Sialylation-associated long non-coding RNA signature predicts the prognosis, tumor microenvironment, and immunotherapy and chemotherapy options in uterine corpus endometrial carcinoma

BackgroundSialylation in uterine corpus endometrial carcinoma (UCEC) differs significantly from apoptotic and ferroptosis pathways. It plays a crucial role in cancer progression and immune response modulation. Exploring how sialylation affects tumor behavior and its link with long non-coding RNAs (lncRNAs) may provide new insights into UCEC prognosis and treatment.MethodsWe obtained RNA transcriptome, clinical, and mutation data of UCEC samples from the TCGA database. Our approach involved developing a risk model based on the co-expression patterns of sialylation genes and lncRNAs. Prognostic lncRNAs were identified through Cox regression and further refined using LASSO analysis. To understand the biological functions and pathways of model-associated differentially expressed genes (MADEGs), we conducted enrichment analyses. We also assessed the immune infiltration status of MADEGs using eight different algorithms, which helped in evaluating the potential for immunotherapy. Additionally, we validated the expression of these lncRNAs in UCEC using cell lines and clinical samples.ResultsWe developed a UCEC risk model using five sialylation-related lncRNAs (AC004884.2, AC026202.2, LINC01579, LINC00942, SLC16A1-AS1). This model, confirmed through Cox analysis and clinical evaluation, effectively predicted patient outcomes. Survival data analysis across entire cohort, as well as within training and test groups, indicated better survival in low-risk UCEC patients. Enrichment analyses linked MADEGs to sialylation functions and cancer pathways. High-risk patients showed increased responsiveness to immune checkpoint inhibitors (ICIs), as indicated by immunological assessments. Subgroup C2 patients showed superior outcomes and a robust response to immunotherapy and chemotherapy. Notably, LINC01579, LINC00942, and SLC16A1-AS1 were significantly overexpressed in UCEC clinical tumor samples as well as in Ishikawa and HEC-1-B cell lines, compared to the normal groups.ConclusionsThis lncRNA signature associated with sialylation could guide prognosis, enhance the understanding of molecular mechanisms, and inform treatment strategies in UCEC. It highlights the potential for the use of ICIs and chemotherapy.

Anoikis-Related Long Non-Coding RNA Signatures to Predict Prognosis and Immune Infiltration of Gastric Cancer.

Anoikis is a distinct type of programmed cell death and a unique mechanism for tumor progress. However, its exact function in gastric cancer (GC) remains unknown. This study aims to investigate the function of anoikis-related lncRNA (ar-lncRNA) in the prognosis of GC and its immunological infiltration. The ar-lncRNAs were derived from RNA sequencing data and associated clinical information obtained from The Cancer Genome Atlas. Pearson correlation analysis, differential screening, LASSO and Cox regression were utilized to identify the typical ar-lncRNAs with prognostic significance, and the corresponding risk model was constructed, respectively. Comprehensive methods were employed to assess the clinical characteristics of the prediction model, ensuring the accuracy of the prediction results. Further analysis was conducted on the relationship between immune microenvironment and risk features, and sensitivity predictions were made about anticancer medicines. A risk model was built according to seven selected ar-lncRNAs. The model was validated and the calibration plots were highly consistent in validating nomogram predictions. Further analyses revealed the great accuracy of the model and its ability to serve as a stand-alone GC prognostic factor. We subsequently disclosed that high-risk groups display significant enrichment in pathways related to tumors and the immune system. Additionally, in tumor immunoassays, notable variations in immune infiltrates and checkpoints were noted between different risk groups. This study proposes, for the first time, that prognostic signatures of ar-lncRNA can be established in GC. These signatures accurately predict the prognosis of GC and offer potential biomarkers, suggesting new avenues for basic research, prognosis prediction and personalized diagnosis and treatment of GC.

A disulfidptosis-associated long noncoding RNA signature to predict low-grade glioma classification, prognosis, tumor microenvironment, and therapy regimens: Observational study.

This study aimed to investigate the function of disulfidptosis-associated long noncoding RNAs (DAlncRNAs) in low-grade gliomas (LGG) through bioinformatics analysis and construct a signature to predict the classification, prognosis, tumor microenvironment, and selection of immunotherapy and chemotherapy in LGG. Genomic, clinical, and mutational information of 526 patients with LGG was retrieved from The Cancer Genome Atlas repository. A nonnegative matrix factorization algorithm was applied to classify patients with LGG. Univariate, LASSO regression, and multivariate Cox regression analyses were performed to determine prognostic DAlncRNAs. Following the median risk score, we defined the sample as a high-risk (HR) or low-risk group. Finally, survival, receiver operating characteristic curve, risk curve, principal component, independent prognosis, risk difference, functional enrichment, tumor microenvironment, immune cell infiltration, mutation, and drug sensitivity analyses were performed. Patients were classified into C1 and C2 subtypes associated with disulfidptosis. Eight prognostic DAlncRNAs (AC003035.2, AC010157.2, AC010273.3, AC011444.3, AC092667.1, AL450270.1, AL645608.2, and LINC01571) were identified, and a prognostic signature of LGG was developed. The DAlncRNA-based signature was found to be an independent prognostic factor in patients with LGG, thereby constructing a nomogram. In addition, in the HR group, immune function was more active and the tumor mutation burden was higher. The patients were mainly composed of subtype C2, and their prognosis was worse. Immunotherapy and chemotherapy were predicted in the HR and low-risk groups, respectively. Our study, based on DAlncRNAs, highlights 2 disulfidptosis-associated LGG subtypes with different prognostic and immune characteristics and creates a novel disulfidptosis-associated prognostic signature, which may inform the classification, prognosis, molecular pathogenesis, and therapeutic strategies for patients with LGG.

Disulfidptosis-related long non-coding RNA signature predicts the prognosis, tumor microenvironment, immunotherapy, and antitumor drug options in colon adenocarcinoma.

This study aims to investigate the role and prognostic significance of long non-coding RNAs (lncRNAs) associated with disulfidptosis in colon adenocarcinoma (COAD). The TCGA database's clinical data and transcriptome profiles were employed. Analysis of previous studies identified 10 disulfidptosis-related genes (DRGs). We used these genes to construct a signature that could independently and accurately predict the prognosis of patients with COAD. The Kaplan-Meier (K-M) curve analysis showed that the lower-risk group had a better prognosis. With the help of multivariate Cox regression analysis, the risk score produced from the patient's signature might independently predict the outcomes. Utilizing a nomogram, the receiver operating characteristic (ROC) curve, and principal component analysis (PCA), the signature's predictive ability was also confirmed. It's interesting to note that immunotherapy, especially PD-1 immune checkpoint suppression, was more likely to benefit low-risk patients. The IC50 levels for certain anticancer agents were lower in the high-risk group. Finally, qRT-PCR analyses in colon cancer cell lines revealed elevated levels of lncRNAs CASC9, ZEB1-AS1, ATP2A1-AS1, SNHG7, AL683813.1, and AP003555.1, and reduced levels of FAM160A1-DT and AC112220.2, compared to normal cell lines. This signature offers insights into prognosis, tumor microenvironment, and options for immunotherapy and antitumor drugs in patients with COAD.

Identification of non-coding RNA signatures in idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a deadly, chronic, progressive, irreversible interstitial lung disease characterized by the formation of scar tissue resulting in permanent lung damage. The average survival time following diagnosis is only 3-5years, with a 5-year survival rate shorter than that of many cancers. Alveolar epithelial cell injury followed by irregular repair is the primary pathological process observed in patients with IPF. An evident characteristic of IPF is the development of fibroblastic foci representing active fibrotic areas. Most of the cells within these foci are believed to be myofibroblasts, which are thought to be the primary source of abnormal extracellular matrix production in IPF. The lung phenotype in IPF is characterized by significantly different processes from healthy lungs, including irregular apoptosis, oxidative stress, and epithelial-mesenchymal transition (EMT) pathways. The exact cause of IPF is not fully understood and remains mysterious. It is not suppressing that non-coding RNAs are involved in the development and progression of IPF. Accordingly, here we aimed to identify non-coding RNA molecules during TGFβ-induced myofibroblast activation. Differential expression and functional enrichment analysis were employed to reveal the impact of non-coding RNAs during TGFβ-associated lung fibrosis. Remarkably, LOC101448202, CZ1P-ASNS, LINC01503, IER3-AS1, MIR503HG, CLMAT3, LINC02593, ACTA2-AS1, LOC102723692, LOC107985728, and LOC105371064 were identified to be differentially altered during TGFβ-stimulated myofibroblast activation. These findings strongly suggest that the mechanism of lung fibrosis is heavily under control of non-coding RNAs, and RNA-based therapies could be a promising approach for future therapeutic interventions to lung fibrosis.

Machine learning-based integration develops a mitophagy-related lncRNA signature for predicting the progression of prostate cancer: a bioinformatic analysis

Prostate cancer remains a complex and challenging disease, necessitating innovative approaches for prognosis and therapeutic guidance. This study integrates machine learning techniques to develop a novel mitophagy-related long non-coding RNA (lncRNA) signature for predicting the progression of prostate cancer. Leveraging the TCGA-PRAD dataset, we identify a set of four key lncRNAs and formulate a riskscore, revealing its potential as a prognostic indicator. Subsequent analyses unravel the intricate connections between riskscore, immune cell infiltration, mutational landscapes, and treatment outcomes. Notably, the pan-cancer exploration of YEATS2-AS1 highlights its pervasive impact, demonstrating elevated expression across various malignancies. Furthermore, drug sensitivity predictions based on riskscore guide personalized chemotherapy strategies, with drugs like Carmustine and Entinostat showing distinct suitability for high and low-risk group patients. Regression analysis exposes significant correlations between the mitophagy-related lncRNAs, riskscore, and key mitophagy-related genes. Molecular docking analyses reveal promising interactions between Cyclophosphamide and proteins encoded by these genes, suggesting potential therapeutic avenues. This comprehensive study not only introduces a robust prognostic tool but also provides valuable insights into the molecular intricacies and potential therapeutic interventions in prostate cancer, paving the way for more personalized and effective clinical approaches.

A circadian rhythm-related lncRNA signature correlates with prognosis and tumor immune microenvironment in head and neck squamous cell carcinoma

ObjectiveTo investigate circadian rhythm-associated long non-coding RNA (lncRNA) signatures in predicting prognosis, metabolism, and immune infiltration in Head and Neck Squamous Cell Carcinoma (HNSC).MethodsHNSC samples were collected from the TCGA database. A signature was constructed using Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) methods. The immune cell infiltration was analyzed using CIBERSORT, ssGSEA, and MCPcounter. The RT-qPCR was used to detect the expression of signature lncRNAs.ResultsA signature comprising 8 lncRNAs was constructed. The constructed signature demonstrated good prognostic prediction capability for HNSC. A nomogram encompassing risk score accurately predicted the long-term OS probability of HNSC. The infiltration levels of T cell, B cell and Macrophages were significantly higher in the high-risk group than in the low-risk group. Cluster analysis showed that the signature lncRNAs could classify the HNSC samples into two clusters. The RT-qPCR suggested that the expression of lncRNAs in signature was consistent with the data in TCGA.ConclusionThe circadian rhythm-associated lncRNA signature has potential as a prognostic indicator for HNSC. It exhibits associations with metabolism, immune microenvironment, and drug sensitivity, thereby providing valuable insights for informing the treatment of HNSC.

Construction of a prognostic model for ovarian cancer based on a comprehensive bioinformatics analysis of cuproptosis-associated long non-coding RNA signatures

Ovarian cancer (OCa) is a common malignancy in women, and the role of cuproptosis and its related genes in OCa is unclear. Using the GSE14407 dataset, we analyzed the expression and correlation of cuproptosis-related genes (CRGs) between tumor and normal groups. From the TCGA-OV dataset, we identified 20 cuproptosis-related long non-coding RNAs (CuLncs) associated with patient survival through univariate Cox analysis. OCa patients were divided into early-stage and late-stage groups to analyze CuLncs expression. Cluster analysis classified patients into two clusters, with Cluster1 having a poorer prognosis. Significant differences in “Lymphatic Invasion” and “Cancer status” were observed between clusters. Seven CRGs showed significant expression differences, validated using the human protein atlas (HPA) databases. Immune analysis revealed a higher ImmuneScore in Cluster1. GSEA identified associated signaling pathways. LASSO regression included 11 CuLncs to construct and validate a survival prediction model, classifying patients into high-risk and low-risk groups. Correlations between riskScore, Cluster phenotype, ImmuneScore, and immune cell infiltration were explored. Cell experiments showed that knocking down AC023644.1 decreases OCa cell viability. In conclusion, we constructed an accurate prognostic model for OCa based on 11 CuLncs, providing a basis for prognosis assessment and potential immunotherapy targets.

Natural products and long noncoding RNA signatures in gallbladder cancer: a review focuses on pathogenesis, diagnosis, and drug resistance.

Gallbladder cancer (GBC) is an aggressive and lethal malignancy with a poor prognosis. Long noncoding RNAs (lncRNAs) and natural products have emerged as key orchestrators of cancer pathogenesis through widespread dysregulation across GBC transcriptomes. Functional studies have revealed that lncRNAs interact with oncoproteins and tumor suppressors to control proliferation, invasion, metastasis, angiogenesis, stemness, and drug resistance. Curcumin, baicalein, oleanolic acid, shikonin, oxymatrine, arctigenin, liensinine, fangchinoline, and dioscin are a few examples of natural compounds that have demonstrated promising anticancer activities against GBC through the regulation of important signaling pathways. The lncRNAs, i.e., SNHG6, Linc00261, GALM, OIP5-AS1, FOXD2-AS1, MINCR, DGCR5, MEG3, GATA6-AS, TUG1, and DILC, are key players in regulating the aforementioned processes. For example, the lncRNAs FOXD2-AS1, DILC, and HOTAIR activate oncogenes such as DNMT1, Wnt/β-catenin, BMI1, and c-Myc, whereas MEG3 and GATA6-AS suppress the tumor proteins NF-κB, EZH2, and miR-421. Clinically, specific lncRNAs can serve as diagnostic or prognostic biomarkers based on overexpression correlating with advanced TNM stage, metastasis, chemoresistance, and poor survival. Therapeutically, targeting aberrant lncRNAs with siRNA or antisense oligos disrupts their oncogenic signaling and inhibits GBC progression. Overall, dysfunctional lncRNA regulatory circuits offer multiple avenues for precision medicine approaches to improve early GBC detection and overcome this deadly cancer. They have the potential to serve as novel biomarkers as they are detectable in bodily fluids and tissues. These findings enhance gallbladder treatments, mitigating resistance to chemo- and radiotherapy.

Identification of a circulating long non-coding RNA signature panel in plasma as a novel biomarker for the detection of acute/early-stage HIV-1 infection

BackgroundIndividuals with acute / early HIV-1 infection are often unaware that they are infected with HIV-1 and may be involved in high-risk behavior leading to transmission of HIV-1. Identifying individuals with acute / early HIV-1 infection is critical to prevent further HIV-1 transmission, as diagnosis can lead to several effective HIV-1 prevention strategies. Identification of disease-stage specific non-viral host biomarkers would be useful as surrogate markers to accurately identify new HIV-1 infections. The goal of this study was to identify a panel of host derived plasma long non-coding RNAs (lncRNAs) that could serve as prognostic and predictive biomarkers to detect early/acute HIV-1 infection.MethodsA total of 84 lncRNAs were analyzed in sixteen plasma samples from HIV-1 infected individuals and four healthy controls using the lncRNA PCR-array. Twenty-one lncRNAs were selected and validated in 80 plasma samples from HIV-1 infected individuals [HIV-1 infected patients in the eclipse stage (n = 20), acute stage (n = 20), post-seroconversion p31 negative stage (n = 20), and post-seroconversion p31 positive stage (n = 20) of infection] and 20 healthy controls. The validation study results were used to develop a plasma lncRNA panel that was evaluated in the panel test phase to detect early/acute HIV-1 infection in 52 independent samples.ResultsWe identified a lncRNA panel (Pmodel−I) containing eight lncRNAs (DISC2, H19, IPW, KRASP1, NEAT1, PRINS, WT1-AS and ZFAS1) that could distinguish HIV-1 infection from healthy controls with high AUC 0·990 (95% CI 0.972-1.000), sensitivity (98.75%), and specificity (95%). We also found that Pmodel−II and Pmodel−III demonstrates 100% sensitivity and specificity (AUC 1·00; 95%CI:1·00–1·00) and could distinguish eclipse stage and acute stage of HIV-1 infection from healthy controls respectively. Antiretroviral treatment (ART) cumulatively restored the levels of lncRNAs to healthy controls levels.ConclusionlncRNA expression changes significantly in response to HIV-1 infection. Our findings also highlight the potential of using circulating lncRNAs to detect both the eclipse and acute stages of HIV-1 infection, which may help to shorten the window period and facilitate early detection and treatment initiation. Initiating ART treatment at this stage would significantly reduce HIV-1 transmission. The differentially expressed lncRNAs identified in this study could serve as potential prognostic and diagnostic biomarkers of HIV-1 infection, as well as new therapeutic targets.

Retraction Note: Breast cancer prognosis and immunological characteristics are predicted using the m6A/m5C/m1A/m7G-related long noncoding RNA signature.

Retraction Note: Breast cancer prognosis and immunological characteristics are predicted using the m6A/m5C/m1A/m7G-related long noncoding RNA signature.

Identification of a methyltransferase-related long noncoding RNA signature as a novel prognosis biomarker for lung adenocarcinoma.

Lung adenocarcinoma (LUAD) accounts for a high proportion of tumor deaths globally, while methyltransferase-related lncRNAs in LUAD were poorly studied. In our study, we focused on two distinct cohorts, TCGA-LUAD and GSE3021, to establish a signature of methyltransferase-related long non-coding RNAs (MeRlncRNAs) in LUAD. We employed univariate Cox and LASSO regression analyses as our main analytical tools. The GSE30219 cohort served as the validation cohort for our findings. Furthermore, to explore the differential pathway enrichments between groups stratified by risk, we utilized Gene Set Enrichment Analysis (GSEA). Additionally, single-sample GSEA (ssGSEA) was conducted to assess the immune infiltration landscape within each sample. Reverse transcription quantitative PCR (RT-qPCR) was also performed to verify the expression of prognostic lncRNAs in both clinically normal and LUAD samples. In LUAD, we identified a set of 32 MeRlncRNAs. We further narrowed our focus to six prognostic lncRNAs to develop gene signatures. The TCGA-LUAD cohort and GSE30219 were utilized to validate the risk score model derived from these signatures. Our analysis showed that the risk score served as an independent prognostic factor, linked to immune-related pathways. Additionally, the analysis of immune infiltration revealed that the immune landscape in high-risk groups was suppressed, which could contribute to poorer prognoses. We also constructed a regulatory network comprising 6 prognostic lncRNAs, 19 miRNAs, and 21 mRNAs. Confirmatory RT-qPCR results aligned with public database findings, verifying the expression of these prognostic lncRNAs in the samples. The prognostic gene signature of LUAD associated with MeRlncRNAs that we provided, may offer us a comprehensive picture of the prognosis prediction for LUAD patients.

A 69 long noncoding RNA signature predicts relapse and acts as independent prognostic factor in pediatric AML

AbstractRisk stratification using genetics and minimal residual disease has allowed for an increase in the cure rates of pediatric acute myeloid leukemia (pedAML) to up to 70% in contemporary protocols. Nevertheless, ∼30% of patients still experience relapse, indicating a need to optimize stratification strategies. Recently, long noncoding RNA (lncRNA) expression has been shown to hold prognostic power in multiple cancer types. Here, we aimed at refining relapse prediction in pedAML using lncRNA expression. We built a relapse–related lncRNA prognostic signature, named AMLlnc69, using 871 transcriptomes of patients with pedAML obtained from the Therapeutically Applicable Research to Generate Effective Treatments repository. We identified a 69 lncRNA signature AMLlnc69 that is highly predictive of relapse risk (c-index = 0.73), with area under the receiver operating characteristic curve (AUC) values for predicting the 1-, 2-, and 3-year relapse-free survival (RFS) of 0.78, 0.77, and 0.77, respectively. The internal validation using a bootstrap method (resampling times = 1000) resulted in a c-index of 0.72 and AUC values for predicting the 1-, 2-, and 3-year RFS of 0.77, 0.76, and 0.76, respectively. Through a Cox regression analysis, AMLlnc69, nucleophosmin mutation, and white blood cell at diagnosis were identified as independent predictors of RFS. Finally, a nomogram was build using these 2 parameters, showing a c-index of 0.80 and 0.71 after bootstrapping (n = 1000). In conclusion, the identified AMLlnc69 will, after prospective validation, add important information to guide the management of patients with pedAML. The nomogram is a promising tool for easy stratification of patients into a novel scheme of relapse-risk groups.

A Novel Cholesterol Metabolism-Related lncRNA Signature Predicts the Prognosis of Patients with Hepatocellular Carcinoma and Their Response to Immunotherapy.

The survival rate of hepatocellular carcinoma (HCC) is low and the prognosis is poor. Metabolic reprogramming is still an emerging hallmark of cancer, and reprogramming of cholesterol metabolism plays a crucial action in tumor pathogenesis. Increasing evidence suggests that cholesterol metabolism affects the cell proliferation, invasion, migration, and resistance to chemotherapy of HCC. To date, no long noncoding RNA (lncRNA) signature associated with cholesterol metabolism has been developed to predict the outcome of patients with HCC. The RNA-seq data as well as the prognostic and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. We conducted univariate and multivariate analyses to assess cholesterol metabolism-related lncRNAs correlated with the prognosis of patients with HCC in order to construct a prognostic signature. Functional differences between low- and high-risk groups were investigated using genomic enrichment analysis (GSEA). Kaplan-Meier (KM) curves were applied to explore the overall survival (OS) of the low- and high-risk groups. Single-sample genomic enrichment analysis (ssGSEA) was applied to investigate the association between this predictive signature and immune function. We subsequently examined how this signature relates to treatment response in HCC patients. A prognostic signature comprising six lncRNAs related to cholesterol metabolism was constructed (AC124798.1, AL031985.3, AC103760.1, NRAV, WAC-AS1 and AC022613.1). We found that low-risk groups showed a better prognosis than high-risk groups. In HCC patients, the cholesterol metabolism-related lncRNA signature may be served as an independent prognostic factor. Cholesterol metabolism-related lncRNA signature had higher diagnostic efficiency compared to clinicopathologic variables. After stratifying patients according to different clinicopathological variables, patients with low-risk had a longer OS compared with high-risk patients. The ssGSEA demonstrated that this signature was closely related to the immune status of HCC patients. GSEA analysis demonstrated that immune- and tumor-related pathways were predominantly enriched in the high-risk group. High-risk patients were more responsive to immune checkpoint inhibitors (ICIs) and conventional chemotherapeutic agents. This cholesterol metabolism-related lncRNA signature can predict the prognosis of HCC patients and guide the clinical management of HCC patients, including immunotherapy.

A novel tumor mutation-related long non-coding RNA signature for predicting overall survival and immunotherapy response in lung adenocarcinoma

BackgroundImmunotherapy has changed the treatment landscape for lung cancer. This study aims to construct a tumor mutation-related model that combines long non-coding RNA (lncRNA) expression levels and tumor mutation levels in tumor genomes to detect the possibilities of the lncRNA signature as an indicator for predicting the prognosis and response to immunotherapy in lung adenocarcinoma (LUAD). MethodsWe downloaded the tumor mutation profiles and RNA-seq expression database of LUAD from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs were extracted based on the cumulative number of mutations. Cox regression analyses were used to identify the prognostic lncRNA signature, and the prognostic value of the five selected lncRNAs was validated by using survival analysis and the receiver operating characteristic (ROC) curve. We used qPCR to validate the expression of five selected lncRNAs between human lung epithelial and human lung adenocarcinoma cell lines. The ImmuCellAI, immunophenoscore (IPS) scores and Tumor Immune Dysfunction and Exclusion (TIDE) analyses were used to predict the response to immunotherapy for this mutation related lncRNA signature. ResultsA total of 162 lncRNAs were detected among the differentially expressed lncRNAs between the Tumor mutational burden (TMB)-high group and the TMB-low group. Then, five lncRNAs (PLAC4, LINC01116, LINC02163, MIR223HG, FAM83A-AS1) were identified as tumor mutation-related candidates for constructing the prognostic prediction model. Kaplan‒Meier curves showed that the overall survival of the low-risk group was significantly better than that of the high-risk group, and the results of the GSE50081 set were consistent. The expression levels of PD1, PD-L1 and CTLA4 in the low-risk group were higher than those in the high-risk group. The IPS scores and TIDE scores of patients in the low-risk group were significantly higher than those in the high-risk group. ConclusionOur findings demonstrated that the five lncRNAs (PLAC4, LINC01116, LINC02163, MIR223HG, FAM83A-AS1) were identified as candidates for constructing the tumor mutation-related model which may serve as an indicator of tumor mutation levels and have important implications for predicting the response to immunotherapy in LUAD.