Prognostic lncRNAs Research Articles

Identification of N7-methylguanosine-related lncRNAs for the risk stratification of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common malignancies in the world. The N7-methylguanosine (m7G) modification is related to the biological processes and regulation of various diseases. This study investigated the role and predictive value of m7G-related long non-coding RNAs (lncRNAs) in HCC. HCC patients were clustered by consensus clustering, and a prognostic signature was developed using Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression analysis. The immune landscape and clinicopathological features of the distinct clusters and subgroups were investigated. A total of 32 m7G-related lncRNAs were confirmed to be prognostic lncRNAs. Two molecular clusters showed significant differences in terms of their clinicopathological features, prognoses, and immune checkpoint gene (ICG) expression levels. Cluster II was associated with upregulated ICG expression and poor overall survival (OS). The Cancer Genome Atlas training cohort was then used to create an m7G-related lncRNA signature for predicting OS. The signature exhibited excellent predictive performance in the training, test, and all cohorts. The high-risk patients had worse clinical outcomes than the low-risk patients. Further study revealed that this signature was an independent prognostic indicator, and a predictive nomogram was developed based on the clinicopathological features and risk score. In addition, we discovered that this model was correlated with ICG expression and tumor immune cell infiltration. Our findings demonstrated that m7G-related lncRNAs are associated with the tumor immune landscape and prognosis and can serve as independent prognostic markers for HCC. These findings provide new insights into the functions of m7G-related lncRNAs in HCC.

A signature of cuproptosis-related lncRNAs predicts prognosis and provides basis for future anti-tumor drug development in breast cancer.

Breast cancer is the most prevalent malignancy worldwide and the leading culprit for women's death. Cuproptosis is a novel and promising modality of tumor cell death and the relationship with long non-coding RNAs (lncRNAs) remains shrouded in a veil. Studies in cuproptosis-related lncRNAs can aid in the clinical management of breast cancer and provide a basis for anti-tumor drug development. RNA-Seq data, somatic mutation data, and clinical information were downloaded from The Cancer Genome Atlas (TCGA). Patients were divided into high- and low-risk groups according to the risk score. Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were used to select prognostic lncRNAs to construct a risk score system. Its' prognostic value was confirmed in the training and validation cohorts subsequently. Functional analysis regarding cuproptosis-related lncRNAs was performed. Eighteen cuproptosis-related lncRNAs were identified and 11 of them including AL023882.1, AC091588.1, AC138028.2, AC027514.1, AL592301.1, LRRC8C-DT, MFF-DT, NIFK-AS1, MECOM-AS1, OTUD6B-AS1 and RNF32-AS1 were selected for risk score system construction. The risk score was confirmed as an independent prognostic factor and patients in the high-risk group had a worse prognosis. A nomogram based on the independent prognostic factors was constructed for clinical decision aids. Further analyses revealed that patients in the high-risk group faced a heavier tumor mutational burden (TMB) and suppressed anti-tumor immunity. Besides, cuproptosis-related lncRNAs were associated with the expression of immune checkpoint inhibitors, N6-adenylate methylation (m6a), and drug sensitivity in breast cancer. A prognostic risk score system with satisfactory predictive accuracy was constructed. Besides, cuproptosis-related lncRNAs can influence the immune microenvironment, TMB, m6a, and drug sensitivity in breast cancer, which may provide a basis for future anti-tumor drug development.

Construction of a novel MPT-driven necrosis-related lncRNAs signature for prognosis prediction in laryngeal squamous cell carcinoma.

Mitochondrial permeability transition (MPT)-driven necrosis, a type of programmed cell death, has recently gained much attention in a variety of tumor types. Few studies have been performed to explore the role of MPT-driven necrosis-related lncRNAs (MPTDNRlncRNAs) in laryngeal squamous cell carcinoma (LSCC). The purpose of our study is to screen MPTDNRlncRNAs with prognostic value and to explore their potential roles in LSCC. The RNA-sequencing data and the corresponding clinical data of LSCC patients were obtained from the TCGA database, and those MPT-driven necrosis-related genes were extracted from the Gene Set Enrichment Analysis (GSEA) database. We identified MPTDNRlncRNAs differentially expressed in LSCC. Also, we gained MPT-driven necrosis-related prognostic lncRNAs by univariate cox regressionanalysis. A novel MPTDNRlncRNAs signature was constructed by LASSO-COX. The accuracy and utility of the MPTDNRlncRNAs signature were evaluated via a variety of statistical methods. Multiple bioinformatics tools were used to explore the underlying difference in biological functions and signaling pathways between the different risk groups. The expressions levels of MPTDNRlncRNAs were analyzed using RT-qPCR in LSCC cell line. Finally, we identified A 5 MPTDNRlncRNAs signature in LSCC. Our prognostic model demonstrated an efficient ability to predict outcomes. The proportion difference of immune cells in the subgroups were significant, such as M0 macrophage and T follicular helper cells. The in vitro experiments suggested that our MPTDNRlncRNAs were significantly different. This 5 MPTDNRlncRNAs signature is a prognostic biomarker for LSCC. More importantly, the novel biologic prognostic model can be utilized for personalized immunotherapy in LSCC patients.

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.

Construction of ceRNA network based on RNA-seq for identifying prognostic lncRNA biomarkers in Perthes disease.

Introduction: Legg-Calvé-Perthes disease or Perthes disease is a condition that occurs in children aged 2 to 15years, and is characterized by osteonecrosis of the femoral head, which results in physical limitations. Despite ongoing research, the pathogenesis and molecular mechanisms underlying the development of Perthes disease remain unclear. In order to obtain further insights, the expression patterns of long non-coding RNAs (lncRNAs), miRNAs, and mRNAs in a rabbit model of Perthes disease were analyzed in this study by transcriptome sequencing. Methods and results: The results of RNA-seq analyses revealed that 77 lncRNAs, 239 miRNAs, and 1027 mRNAs were differentially expressed in the rabbit model. This finding suggested that multiple genetic pathways are involved in the development of Perthes disease. A weighted gene co-expression network analysis (WGCNA) network was subsequently constructed using the differentially expressed mRNAs (DEmRNAs), and network analysis revealed that the genes associated with angiogenesis and platelet activation were downregulated, which was consistent with the findings of Perthes disease. A competing endogenous RNA (ceRNA) network was additionally constructed using 29 differentially expressed lncRNAs (including HIF3A and LOC103350994), 28 differentially expressed miRNAs (including ocu-miR-574-5p and ocu-miR-324-3p), and 76 DEmRNAs (including ALOX12 and PTGER2). Disscusion: The results obtained herein provide novel perspectives regarding the pathogenesis and molecular mechanisms underlying the development of Perthes disease. The findings of this study can pave the way for the development of effective therapeutic strategies for Perthes disease in future.

Construction of a Cuproptosis-Related lncRNA Prognostic Model for Bladder Urothelial Carcinoma and Screening of Potential Drugs

Objective. This study aimed to analyze the cuproptosis-related long non-coding RNA (lncRNA) in patients with bladder urothelial carcinoma (BLCA), construct a prognostic model, and screen its potential drugs. Methods. The transcriptome expression and clinical and mutation burden data related to BLCA were downloaded from The Cancer Genome Atlas database. The prognostic lncRNAs were screened using univariate Cox and Lasso regression analyses, and then included in the multifactor risk ratio model. The risk score of each sample was calculated based on the prognostic model formula, and the patients were divided into high- and low-risk groups for survival difference analysis. Clinically relevant receiver operating characteristic (ROC) curve, C-index principal component analysis, and clinical data statistics were used to evaluate the predictive power of the model. The risk-differential lncRNAs were functionally enriched. We calculated the tumor mutation burden of risk lncRNAs, and survival and the Tumor Immune Dysfunction and Exclusion analyses for high- and low-risk groups. Finally, immunocorrelation analysis and potential drug screening were performed. Results. Eleven lncRNAs with independent prognostic significance were screened out to construct the prognostic model. Survival analysis showed a significant difference in survival between the high- and low-risk groups. The areas under the ROC curve at 1, 3, and 5 years were 0.711, 0.679, and 0.713, respectively. The discrimination between the lncRNA high- and low-risk groups in the constructed model was the most obvious. The risk-differential lncRNAs were closely related to immunity. The treatment drugs with high sensitivity were screened based on the IC50 value. Conclusion. The 11 cuproptosis-related lncRNAs may serve as molecular biomarkers and therapeutic targets for BLCA.

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.

A novel cuproptosis-related lncRNA signature predicts the prognosis and immunotherapy for hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most serious malignant tumors with a poor prognosis worldwide. Cuproptosis is a novel copper-dependent cell death form, involving mitochondrial respiration and lipoylated components of the tricarboxylic acid (TCA) cycle. Long non-coding RNAs (lncRNAs) have been demonstrated to affect the tumorigenesis, growth, and metastasis of HCC. We explored the potential roles of cuproptosis-related lncRNAs in predicting the prognosis for HCC. The RNA-seq transcriptome data, mutation data, and clinical information data of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. The least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression analyses were performed to identify a prognostic cuproptosis-related lncRNA signature. The receiver operating characteristic (ROC) analysis was used to evaluate the predictive value of the lncRNA signature for HCC. The enrichment pathways, immune functions, immune cell infiltration, tumor mutation burden, and drug sensitivity were also analyzed. We constructed a prognostic model consisting of 8 cuproptosis-related lncRNAs for HCC. The patients were divided into high-risk group and low-risk group according to the riskscore calculated using the model. Kaplan-Meier analysis revealed that the high-risk lncRNA signature was correlated with poor overall survival [hazard ratio (HR) =1.009, 95% confidence interval (CI) = 1.002-1.015; p= 0.010)] of HCC. A prognostic nomogram incorporated the lncRNA signature and clinicopathological features were constructed and showed favorable performance for predicting prognosis of HCC patients. In addition, the most immune-related functions were significantly different between the high-risk and low-risk groups. Tumor mutation burden (TMB) and immune checkpoints were also expressed differently between the two risk groups. Finally, HCC patients with low-risk score were more sensitive to several chemotherapy drugs. The novel cuproptosis-related lncRNA signature could be used to predict prognosis and evaluate the effect of chemotherapy for HCC.

Identification and validation of m5c-related lncRNA risk model for ovarian cancer

Ovarian cancer (OC) is one of the common malignant tumors that seriously threaten women's health, and there is a lack of clinical prognostic predictors, while m5c and lncRNA have been shown to be predictive of multiple cancers, including OC. Therefore, our goal was to construct a risk model for OC based on m5c-related lncRNA.340 m5c-related lncRNA were identified and a novel risk model of OC ground on nine m5C-related lncRNA was constructed using LASSO-COX regression analysis. Kaplan–Meier analysis showed there was a significant difference in prognosis between risk groups. We established a nomogram which was a good predictor of overall survival. In addition, GSEA was enriched in multiple pathways and immune function analysis suggested that immune infiltration varies depending on the risk group. In vitro experiments show that AC005562.1, a key lncRNA of the risk model, is highly expressed in OC cells and promotes OC cell proliferation. Finally, we further explored the potential biological markers of m5c-related lncRNA in OC with WGCNA analysis and established a ceRNA network. In conclusion,we have developed a reliable m5c-related prediction model and performed systematic validation and exploration of various aspects. These results can be used for the assessment of OC prognosis and the discovery of novel biomarkers.

Genome instability-related LINC02577, LINC01133 and AC107464.2 are lncRNA prognostic markers correlated with immune microenvironment in pancreatic adenocarcinoma

BackgroundPancreatic adenocarcinoma (PAAD) is a leading cause of malignancy-related deaths worldwide, and the efficacy of immunotherapy on PAAD is limited. Studies report that long non-coding RNAs (lncRNAs) play an important role in modulating genomic instability and immunotherapy. However, the identification of genome instability-related lncRNAs and their clinical significance has not been investigated in PAAD.MethodsThe current study developed a computational framework for mutation hypothesis based on lncRNA expression profile and somatic mutation spectrum in pancreatic adenocarcinoma genome. We explored the potential of GInLncRNAs(genome instability-related lncRNAs) through co-expression analysis and function enrichment analysis. We further analyzed GInLncRNAs by Cox regression and used the results to construct a prognostic lncRNA signature. Finally, we analyzed the relationship between GILncSig (genomic instability derived 3-lncRNA signature) and immunotherapy.ResultsA GILncSig was developed using bioinformatics analyses. It could divide patients into high-risk and low-risk groups, and there was a significant difference in OS between the two groups. In addition, GILncSig was associated with genome mutation rate in pancreatic adenocarcinoma, indicating its potential value as a marker for genomic instability. The GILncSig accurately grouped wild type patients of KRAS into two risk groups. The prognosis of the low-risk group was significantly improved. GILncSig was significantly correlated with the level of immune cell infiltration and immune checkpoint.ConclusionsIn summary, the current study provides a basis for further studies on the role of lncRNA in genomic instability and immunotherapy. The study provides a novel method for identification of cancer biomarkers related to genomic instability and immunotherapy.

Mining glycosylation-related prognostic lncRNAs and constructing a prognostic model for overall survival prediction in glioma: A study based on bioinformatics analysis.

Dysregulation of protein glycosylation plays a crucial role in the development of glioma. Long noncoding RNA (lncRNAs), functional RNA molecules without protein-coding ability, regulate gene expression and participate in malignant glioma progression. However, it remains unclear how lncRNAs are involved in glycosylation glioma malignancy. Identification of prognostic glycosylation-related lncRNAs in gliomas is necessary. We collected RNA-seq data and clinicopathological information of glioma patients from the cancer genome atlas and Chinese glioma genome atlas. We used the "limma" package to explore glycosylation-related gene and screened related lncRNAs from abnormally glycosylated genes. Using univariate Cox analyses Regression and least absolute shrinkage and selection operator analyses, we constructed a risk signature with 7 glycosylation-related lncRNAs. Based on the median risk score (RS), patients with gliomas were divided into low- and high-risk subgroups with different overall survival rates. Univariate and multivariate Cox analyses regression analyses were performed to assess the independent prognostic ability of the RS. Twenty glycosylation-related lncRNAs were identified by univariate Cox regression analyses. Two glioma subgroups were identified using consistent protein clustering, with the prognosis of the former being better than that of the latter. Least absolute shrinkage and selection operator analysis identified 7 survival RSs for glycosylation-related lncRNAs, which were identified as independent prognostic markers and predictors of glioma clinicopathological features. Glycosylation-related lncRNAs play an important role in the malignant development of gliomas and may help guide treatment options.

Identification of cuproptosis-related lncRNAs to predict prognosis and immune infiltration characteristics in alimentary tract malignancies

BackgroundAlimentary tract malignancies (ATM) caused nearly one-third of all tumor-related death. Cuproptosis is a newly identified cell death pattern. The role of cuproptosis-associated lncRNAs in ATM is unknown.MethodData from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used to identify prognostic lncRNAs by Cox regression and LASSO. Then a predictive nomogram was constructed based on seven prognostic lncRNAs. In addition, the prognostic potential of the seven-lncRNA signature was verified via survival analysis, the receiver operating characteristic (ROC) curve, calibration curve, and clinicopathologic characteristics correlation analysis. Furthermore, we explored the associations between the signature risk score and immune landscape, and somatic gene mutation.ResultsWe identified 1211 cuproptosis-related lncRNAs and seven survival-related lncRNAs. Patients were categorized into high-risk and low-risk groups with significantly different prognoses. ROC and calibration curve confirmed the good prediction capability of the risk model and nomogram. Somatic mutations between the two groups were compared. We also found that patients in the two groups responded differently to immune checkpoint inhibitors and immunotherapy.ConclusionThe proposed novel seven lncRNAs nomogram could predict prognosis and guide treatment of ATM. Further research was required to validate the nomogram.

Identification Invasion-Related Long Non-Coding RNAs in Lung Adenocarcinoma and Analysis of Competitive Endogenous RNA Regulatory Networks.

Cell invasion plays a vital role in cancer development and progression. Aberrant expression of long non-coding RNAs (lncRNAs) is also critical in carcinogenesis. However, the prognostic value of invasion-related lncRNAs in lung adenocarcinoma (LUAD) remains unknown. Differentially expressed mRNAs (DEmRNAs), lncRNAs (DElncRNAs), and microRNAs (DEmiRNAs) were between LUAD and control samples. Pearson correlation analyses were performed to screen for invasion-related DElncRNAs (DEIRLs). Univariate and multivariate Cox regression algorithms were applied to identify key genes and construct the risk score model, which was evaluated using receiver operating characteristic (ROC) curves. Gene set enrichment analysis (GSEA) was used to explore the underlying pathways of the risk model. Moreover, an invasion-related competitive endogenous RNA (ceRNA) regulatory network was constructed. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was performed to detect the expression of prognostic lncRNAs in the LUAD and control samples. A total of 45 DElncRNAs were identified as DEIRLs. RP3-525N10.2, LINC00857, EP300-AS1, PDZRN3-AS1, and RP5-1102E8.3 were potential prognostic lncRNAs, the expression of which was verified by RT-qPCR in LUAD samples. Both the risk score model and nomogram used the prognostic lncRNAs. ROC curves showed the risk score model had moderate accuracy and the nomogram had high accuracy in predicting patient prognosis. GSEA results indicated that the risk score model was associated with many biological processes and pathways relevant to cell proliferation. A ceRNA regulatory network was constructed in which PDZRN3-miR-96-5p-CPEB1, EP300-AS1-miR-93-5p-CORO2B, and RP3-525N10.2-miR-130a-5p-GHR may be key invasion-related regulatory pathways in LUAD. Our study identified five novel invasion-related prognostic lncRNAs (RP3-525N10.2, LINC00857, EP300-AS1, PDZRN3-AS1, and RP5-1102E8.3) and established an accurate model for predicting the prognosis of patients with LUAD. These findings enrich our understanding of the relationships between cell invasion, lncRNAs, and LUAD and may provide novel treatment directions.

Cuproptosis-related lncRNAs forecast the prognosis of acute myeloid leukemia.

Acute myeloid leukemia (AML) is a highly heterogeneous cluster of hematologic malignancies. Leukemic stem cells (LSCs) are one of the culprits for the persistence and relapse of AML. The discovery of copper-induced cell death, namely cuproptosis, gives bright insights into the treatment of AML. Analogous to copper ions, long non-coding RNAs (lncRNAs) are not bystanders for AML progression, especially for LSC physiology. Uncovering the involvement of cuproptosis-related lncRNAs in AML will benefit clinical management. Detection of prognostic relevant cuproptosis-related lncRNAs are carried out by Pearson correlation analysis and univariate Cox analysis with RNA sequencing data of The Cancer Genome Atlas-Acute Myeloid Leukemia (TCGA-LAML) cohort. After the least absolute shrinkage and selection operator (LASSO) regression and the subsequent multivariate Cox analysis, a cuproptosis-related risk score (CuRS) system was derived to weigh the risk of AML patients. Thereafter, AML patients were classified into two groups by their risk property which was validated with principal component analysis (PCA), risk curves, Kaplan-Meier survival analysis, the combined receiver operating characteristic (ROC) curves, and nomogram. Variations in biological pathways and divergences in immune infiltration and immune-related processes between groups were resolved by GSEA and CIBERSORT algorism, respectively. Response to chemotherapies were scrutinized as well. The expression profiles of the candidate lncRNAs were examined by real-time quantitative polymerase chain reaction (RT-qPCR) and the specific mechanisms of lncRNA FAM30A were determined by transcriptomic analysis. We fabricated an efficient prognostic signature named CuRS incorporating 4 lncRNAs (TRAF3IP2-AS1, NBR2, TP53TG1, and FAM30A) relevant to immune environment and chemotherapy responsiveness. The relevance of lncRNA FAM30A with proliferation, migration ability, Daunorubicin resistance and its reciprocal action with AUF1 were demonstrated in an LSC cell line. Transcriptomic analysis suggested correlations between FAM30A and T cell differentiation and signaling, intercellular junction genes. The prognostic signature CuRS can guide prognostic stratification and personalized AML therapy. Analysis of FAM30A offers a foundation for investigating LSC-targeted therapies.

Novel chemokine related LncRNA signature correlates with the prognosis, immune landscape, and therapeutic sensitivity of esophageal squamous cell cancer

BackgroundEsophageal squamous cell carcinoma (ESCC) is closely correlated with malignant biological characteristics and poor survival. Recently, chemokines have been reported to be involved in the progression of tumors, and they can also regulate the tumor microenvironment. However, it is unclear whether chemokine-related long noncoding RNAs (lncRNAs) affect the prognosis of ESCC.MethodsWe downloaded RNA-seq and clinical data from the Gene Expression Omnibus (GEO database. Chemokine-related lncRNAs were screened by differential analysis and Pearson correlation analysis. Then, prognosis-related lncRNAs were screened by using univariate COX regression, and risk models were constructed after the least absolute shrinkage and selection operator (LASSO) regression and multivariate COX regression. The predictive value of the signature was assessed using Kaplan–Meier test, time-dependent receiver operating characteristic (ROC) curves, decision curve analysis (DCA) and calibration curve. Moreover, a nomogram to predict patients’ 1-year 3-year and 5-year prognosis was constructed. Gene set enrichment analyses (GSEA), Gene Ontology/Kyoto Encyclopedia of Genes and Genomes (GO/KEGG), evaluation of immune cell infiltration, and estimation of drug sensitivity were also conducted.ResultsIn this study, 677 chemokine-related lncRNAs were first obtained by differential analysis and Pearson correlation. Then, six chemokine-related lncRNAs were obtained by using univariate COX, LASSO and multivariate COX to construct a novel chemokine-related lncRNAs risk model. The signature manifested favorable predictive validity and accuracy both in the testing and training cohorts. The chemokine-related signature could classify ESCC patients into two risk groups well, which indicated that high-risk group exhibited poor prognostic outcome. In addition, this risk model played an important role in predicting signaling pathways, immune cell infiltration, stromal score, and drug sensitivity in ESCC patients.ConclusionsThese findings elucidated the critical role of novel prognostic chemokine-related lncRNAs in prognosis, immune landscape, and drug therapy, thus throwing light on prognostic evaluation and therapeutic targets for ESCC patients.

Significance of cuproptosis-related lncRNA signature in LUAD prognosis and immunotherapy: A machine learning approach.

Cuproptosis-related genes are closely related to lung adenocarcinoma (LUAD), which can be analyzed via the analysis of long noncoding RNA (lncRNA). To date, the clinical significance and function of cuproptosis-related lncRNAs are still not well elucidated. Further analysis of cuproptosis-related prognostic lncRNAs is of great significance for the treatment, diagnosis, and prognosis of LUAD. In this study, a multiple machine learning (ML)-based computational approach was proposed for the identification of the cuproptosis-related lncRNAs signature (CRlncSig) via comprehensive analysis of cuproptosis, lncRNAs, and clinical characteristics. The proposed approach integrated multiple ML algorithms (least absolute shrinkage and selection operator regression analysis, univariate and multivariate Cox regression) to effectively identify the CRlncSig. Based on the proposed approach, the CRlncSig was identified from the 3450 cuproptosis-related lncRNAs, which consist of 13 lncRNAs (CDKN2A-DT, FAM66C, FAM83A-AS1, AL359232.1, FRMD6-AS1, AC027237.4, AC023090.1, AL157888.1, AL627443.3, AC026355.2, AC008957.1, AP000346.1, and GLIS2-AS1). The CRlncSig could well predict the prognosis of different LUAD patients, which is different from other clinical features. Moreover, the CRlncSig was proved to be an effective indicator of patient survival via functional characterization analysis, which is relevant to cancer progression and immune infiltration. Furthermore, the results of RT-PCR assay indicated that the expression level of FAM83A-AS1 and AC026355.2 in A549 and H1975 cells (LUAD) was significantly higher than that in BEAS-2B cells (normal lung epithelial).

Analysis of the prognostic significance and potential mechanisms of lncRNAs related to m6A methylation in laryngeal cancer

ABSTRACT Objective to investigate the prognostic significance and potential mechanism analysis of m6A methylation-associated lncRNAs in laryngeal cancer. Methods based on the expression of m6A-associated lncRNAs, the samples were divided into two clusters and least absolute value and selection operator (LASSO) regression analysis was performed to build and validate prognostic models. In addition, the relationships between risk scores, clusters, arginine synthase (SMS), tumor microenvironment, clinicopathological features, immune infiltration, immune checkpoints, and tumor mutation burden were analyzed. Finally, the relationship between SMS and m6A-associated IncRNAs was analyzed and SMS-associated pathways were enriched by gene set enrichment analysis (GSEA). Results a total of 95 lncRNAs were associated with the expression of 22 m6A methylation regulators in laryngeal cancer, 14 of which were prognostic lncRNAs. These lncRNAs were divided into two clusters and evaluated. Clinicopathological features did not show significant differences. However, the two clusters differed significantly in terms of naive B cells, memory B cells, naive CD4 T cells, T helper cells and immune score. lASSO regression analysis showed that risk score was a significant predictor of progression-free survival. Conclusion low expression of m6A-related lncRNAs involved in laryngeal cancer development in laryngeal cancer tissues can be used as an indicator to diagnose patients with laryngeal cancer, reduce patient prognosis, be an independent risk factor affecting patient prognosis and be able to assess patient prognosis.

Construction of an m6A-related lncRNA model for predicting prognosis and immunotherapy in patients with lung adenocarcinoma.

N6-methyladenosine (m6A)-related lncRNAs could be involved in the development of multiple tumors with an unknown role in lung adenocarcinoma (LUAD). Hence, gene expression data and clinical data of LUAD patients were acquired from The Cancer Genome Atlas Database. The prognostic m6A-related lncRNAs were identified through differential lncRNA expression analysis and Spearman's correlation analysis. The least absolute shrinkage and selection operator regression was used to establish the prognostic risk model, so as to evaluate and validate the predictive performance with survival analysis and receiver operating characteristic curve analysis. The expression of immune checkpoints, immune cell infiltration and drug sensitivity of patients in different risk groups were analyzed separately. A total of 19 prognostic m6A-related lncRNAs were identified to set up the prognostic risk model. The patients were divided into high- and low-risk groups based on the median value of the risk scores. Compared with the patients in the low-risk group, the prognosis of the patients in the high-risk group was relatively worse. The receiver operating characteristic curves indicated that this model had excellent sensitivity and specificity. Multivariate Cox regression analysis demonstrated that the risk score could be supposed as an independent prognostic risk factor. We highlighted that the risk scores were correlated with immune cell infiltration and drug sensitivity for constructing a prognostic risk model in LUAD patients based on m6A-related lncRNAs.

A novel prognostic related lncRNA signature associated with amino acid metabolism in glioma.

Glioma is one of the deadliest malignant brain tumors in adults, which is highly invasive and has a poor prognosis, and long non-coding RNAs (lncRNAs) have key roles in the progression of glioma. Amino acid metabolism reprogramming is an emerging hallmark in cancer. However, the diverse amino acid metabolism programs and prognostic value remain unclear during glioma progression. Thus, we aim to find potential amino-related prognostic glioma hub genes, elaborate and verify their functions, and explore further their impact on glioma. Glioblastoma (GBM) and low-grade glioma (LGG) patients' data were downloaded from TCGA and CCGA datasets. LncRNAs associated with amino acid metabolism were discriminated against via correlation analysis. LASSO analysis and Cox regression analysis were conducted to identify lncRNAs related to prognosis. GSVA and GSEA were performed to predict the potential biological functions of lncRNA. Somatic mutation data and CNV data were further built to demonstrate genomic alterations and the correlation between risk scores. Human glioma cell lines U251 and U87-MG were used for further validation in vitro experiments. There were eight amino-related lncRNAs in total with a high prognostic value that were identified via Cox regression and LASSO regression analyses. The high risk-score group presented a significantly poorer prognosis compared with the low risk-score group, with more clinicopathological features and characteristic genomic aberrations. Our results provided new insights into biological functions in the above signature lncRNAs, which participate in the amino acid metabolism of glioma. LINC01561 is one of the eight identified lncRNAs, which was adopted for further verification. In in vitro experiments, siRNA-mediated LINC01561 silencing suppresses glioma cells' viability, migration, and proliferation. Novel amino-related lncRNAs associated with the survival of glioma patients were identified, and a lncRNA signature can predict glioma prognosis and therapy response, which possibly has vital roles in glioma. Meanwhile, it emphasized the importance of amino acid metabolism in glioma, particularly in providing deeper research at the molecular level.

A Novel Aging-Related Prognostic lncRNA Signature Correlated with Immune Cell Infiltration and Response to Immunotherapy in Breast Cancer.

Breast cancer (BC) is among the most universal malignant tumors in women worldwide. Aging is a complex phenomenon, caused by a variety of factors, that plays a significant role in tumor development. Consequently, it is crucial to screen for prognostic aging-related long non-coding RNAs (lncRNAs) in BC. The BC samples from the breast-invasive carcinoma cohort were downloaded from The Cancer Genome Atlas (TCGA) database. The differential expression of aging-related lncRNAs (DEarlncRNAs) was screened by Pearson correlation analysis. Univariate Cox regression, LASSO-Cox analysis, and multivariate Cox analysis were performed to construct an aging-related lncRNA signature. The signature was validated in the GSE20685 dataset from the Gene Expression Omnibus (GEO) database. Subsequently, a nomogram was constructed to predict survival in BC patients. The accuracy of prediction performance was assessed through the time-dependent receiver operating characteristic (ROC) curves, Kaplan-Meier analysis, principal component analyses, decision curve analysis, calibration curve, and concordance index. Finally, differences in tumor mutational burden, tumor-infiltrating immune cells, and patients' response to chemotherapy and immunotherapy between the high- and low-risk score groups were explored. Analysis of the TCGA cohort revealed a six aging-related lncRNA signature consisting of MCF2L-AS1, USP30-AS1, OTUD6B-AS1, MAPT-AS1, PRR34-AS1, and DLGAP1-AS1. The time-dependent ROC curve proved the optimal predictability for prognosis in BC patients with areas under curves (AUCs) of 0.753, 0.772, and 0.722 in 1, 3, and 5 years, respectively. Patients in the low-risk group had better overall survival and significantly lower total tumor mutational burden. Meanwhile, the high-risk group had a lower proportion of tumor-killing immune cells. The low-risk group could benefit more from immunotherapy and some chemotherapeutics than the high-risk group. The aging-related lncRNA signature can provide new perspectives and methods for early BC diagnosis and therapeutic targets, especially tumor immunotherapy.