Related lncRNAs Research Articles

Autophagy-related LncRNA PRDM10-DT responds to UVB radiation in keratinocytes

Ultraviolet (UV)-B radiation is a major environmental risk factor that is responsible for the development and progression of many skin disorders. Autophagy is the process of degradation and recycling of damaged cytoplasmic organelles, macromolecular aggregates, and long-lived proteins. Previously, we found that the autophagy inducer apigenin restored UVB-impaired autophagy and the cellular response by downregulating the expression of autophagy-related genes such as ATG5. To explore long noncoding RNAs (lncRNAs) involved in regulating these autophagy-related genes, in this study, we assessed the expression profiles of lncRNAs and mRNAs using a microarray in human epidermal keratinocytes (HEKs) treated with or without apigenin after UVB radiation. The expression levels of 80 selected autophagy-related genes and related lncRNAs were confirmed by quantitative real-time polymerase chain reaction (qRT‒PCR). The lncRNA PRDM10-DT was proposed to regulate IRGM based on the ceRNA and coexpression pattern and was demonstrated to be involved in autophagy regulation, proliferation and migration of HEKs by qRT‒PCR, Western blotting, colony formation and scratch wound assays, respectively. These findings suggest an autophagy-related lncRNA in response to UVB radiation that promotes the proliferation and migration of HEKs through inducing autophagy by competing microRNAs for IRGM.

Construction and validation of a cuproptosis-related lncRNA prognosis signature in bladder carcinoma.

Bladder cancer (BLCA) is a prevalent urological tumor with high morbidity and mortality. However, BLCA treatment remains challenging due to a lack of effective biomarkers. Long non-coding RNAs (lncRNAs), as active participants in tumor progression are involved in multiple biological regulatory mechanisms, and cuproptosis-related genes participate in the development of cancer. It is important to discover cuproptosis- related lncRNAs for BLCA diagnosis and treatment. A predictive signature was constructed based on least absolute shrinkage and selection operator regression (LASSO) and Cox regression analyses of the 9 cuproptosis-related lncRNAs. Samples were divided into high-risk group and low-risk group based on their median risk scores to explore their prognosis. This signature is well predictive, as evidenced by the receiver operating characteristic curves (ROC curves) and K-M curves. Based on the nomogram, we were able to visually forecast the survival rates of patients with BLCA at 1-, 3-, and 5-year, and the calibration plots displayed that the actual results were well matched with the predicted 1-, 3-, and 5-year survival rates. Furthermore, BLCA patients in the high-risk group had a higher Tumor Immune Dysfunction and Exclusion (TIDE) score and lower TMB. Finally, we investigated the response of antitumor drugs for BLCA patients in different risk groups, and a statistically significant difference was observed in the sensitivity of those drugs between low- and the high-risk groups. According to the 9 cuproptosis-related lncRNAs, we constructed a signature which can be served as a promising prognostic biomarker for BLCA patients.

M6A-related lncRNAs predict prognosis and indicate cell cycle in gastric cancer.

Background: N6-methyladenosine (m6A) modification is a common epigenetic methylation modification of RNA, which plays an important role in gastric carcinogenesis and progression by regulating long non-coding RNA (lncRNA). This study is aimed to investigate the potential prognostic signatures of m6A -related lncRNAs in STAD. Methods: The m6A-related lncRNAs with the most significant impact on gastric cancer prognosis in the TCGA database were identified by bioinformatics and machine learning methods. The m6A-related lncRNA prognostic model (m6A-LPS) and nomogram was constructed by Cox regression analysis with the minimum absolute contraction and selection operator (LASSO) algorithm. The functional enrichment analysis of m6A-related lncRNAs was also investigated. The miRTarBase, miRDB and TargetScan databases were utilized to establish a prognosis-related network of competing endogenous RNA (ceRNA) by bioinformatics methods. The correlation of AL391152.1 expressions and cell cycle were experimentally testified by qRT-PCR and flow cytometry. Results: In total, 697 lncRNAs that were identified as m6A-related lncRNAs in GC samples. The survival analysis showed that 18 lncRNAs demonstrated prognostic values. A risk model with 11 lncRNAs was established by Lasso Cox regression, and can predict the prognosis of GC patients. Cox regression analysis and ROC curve indicated that this lncRNA prediction model was an independent risk factor for survival rates. Functional enrichment analysis and ceRNA network revealed that the nomogram was notably associated with cell cycle. qRT-PCR and flow cytometry revealed that downregulation of GC m6A-related lncRNA AL391152.1 could decrease cyclins expression in SGC7901 cells. Conclusion: A m6A-related lncRNAs prognostic model was established in this study, which can be applied to predict prognosis and cell cycle in gastric cancer.

I-125 seeds brachytherapy combined with immunotherapy for MET amplification in non-small cell lung cancer from clinical application to related lncRNA mechanism explore: a case report.

Advanced non-small cell lung cancer (NSCLC) with MET amplification primarily relies on MET inhibitors for treatment, but once resistance occurs, the available treatment options are limited and the prognosis is typically poor. A 57-year-old man with advanced NSCLC and C-MET amplification was initially treated with crizotinib but developed progressive disease. After the antirotinib treatment, he achieved a partial response for a year. Genetic testing showed high PD-L1 expression, and he was treated with pembrolizumab and chemotherapy for 3months, with partial response. Maintenance therapy with pembrolizumab and local I-125 seeds brachytherapy (ISB) was given after the lung lesion progressed but other lesions remained stable. The therapy resulted in significant resolution of the right upper lung lesion. It demonstrates the effectiveness of ISB-ICI combination in treating MET amplification advanced NSCLC. Ongoing research and treatment innovation are important in managing advanced NSCLC with complex genetic aberrations. To explore the candidate mechanism of ISB therapy response, we download public genetic data and conduct different expression Lncrnas analysis and pathway analysis to discover radiotherapy related sensitive or resistance lncRNAs and pathways, we found that AL654754.1 is a key lncRNA with radiotherapy response, and it also include in classical p53 and Wnt signaling pathway. Overall, the clinical case reports, combined with the exploration of underlying mechanisms, provide positive guidance for the precise treatment of lung cancer.

5-Methylcytosine-related lncRNAs: predicting prognosis and identifying hot and cold tumor subtypes in head and neck squamous cell carcinoma

Background5-Methylcytosine (m5C) methylation is recognized as an mRNA modification that participates in biological progression by regulating related lncRNAs. In this research, we explored the relationship between m5C-related lncRNAs (mrlncRNAs) and head and neck squamous cell carcinoma (HNSCC) to establish a predictive model.MethodsRNA sequencing and related information were obtained from the TCGA database, and patients were divided into two sets to establish and verify the risk model while identifying prognostic mrlncRNAs. Areas under the ROC curves were assessed to evaluate the predictive effectiveness, and a predictive nomogram was constructed for further prediction. Subsequently, the tumor mutation burden (TMB), stemness, functional enrichment analysis, tumor microenvironment, and immunotherapeutic and chemotherapeutic responses were also assessed based on this novel risk model. Moreover, patients were regrouped into subtypes according to the expression of model mrlncRNAs.ResultsAssessed by the predictive risk model, patients were distinguished into the low-MLRS and high-MLRS groups, showing satisfactory predictive effects with AUCs of 0.673, 0.712, and 0.681 for the ROCs, respectively. Patients in the low-MLRS groups exhibited better survival status, lower mutated frequency, and lower stemness but were more sensitive to immunotherapeutic response, whereas the high-MLRS group appeared to have higher sensitivity to chemotherapy. Subsequently, patients were regrouped into two clusters: cluster 1 displayed immunosuppressive status, but cluster 2 behaved as a hot tumor with a better immunotherapeutic response.ConclusionsReferring to the above results, we established a m5C-related lncRNA model to evaluate the prognosis, TME, TMB, and clinical treatments for HNSCC patients. This novel assessment system is able to precisely predict the patients’ prognosis and identify hot and cold tumor subtypes clearly for HNSCC patients, providing ideas for clinical treatment.

Long non-coding RNAs and rheumatoid arthritis: Pathogenesis and clinical implications.

Long non-coding RNAs (lncRNAs) are a class of noncoding RNAs with a length larger than 200 nucleotides that participate in various diseases and biological processes as they can control gene expression by different mechanisms. Rheumatoid arthritis (RA) is an inflammatory autoimmune disorder characterized by symmetrical destructive destruction of distal joints as well as extra-articular involvement. Different studies have documented and proven the abnormal expression of lncRNAs in RA patients. Various lncRNAs have proven potential as biomarkers and targets for diagnosing, prognosis and treating RA. This review will focus on RA pathogenesis, clinical implications, and related lncRNA expressions that help to identify new biomarkers and treatment targets.

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.

Oxaliplatin related lncRNAs prognostic models predict the prognosis of patients given oxaliplatin-based chemotherapy

BackgroundOxaliplatin-based chemotherapy is the first-line treatment for colorectal cancer (CRC). Long noncoding RNAs (lncRNAs) have been implicated in chemotherapy sensitivity. This study aimed to identify lncRNAs related to oxaliplatin sensitivity and predict the prognosis of CRC patients underwent oxaliplatin-based chemotherapy.MethodsData from the Genomics of Drug Sensitivity in Cancer (GDSC) was used to screen for lncRNAs related to oxaliplatin sensitivity. Four machine learning algorithms (LASSO, Decision tree, Random-forest, and support vector machine) were applied to identify the key lncRNAs. A predictive model for oxaliplatin sensitivity and a prognostic model based on key lncRNAs were established. The published datasets, and cell experiments were used to verify the predictive value.ResultsA total of 805 tumor cell lines from GDSC were divided into oxaliplatin sensitive (top 1/3) and resistant (bottom 1/3) groups based on their IC50 values, and 113 lncRNAs, which were differentially expressed between the two groups, were selected and incorporated into four machine learning algorithms, and seven key lncRNAs were identified. The predictive model exhibited good predictions for oxaliplatin sensitivity. The prognostic model exhibited high performance in patients with CRC who underwent oxaliplatin-based chemotherapies. Four lncRNAs, including C20orf197, UCA1, MIR17HG, and MIR22HG, displayed consistent responses to oxaliplatin treatment in the validation analysis.ConclusionCertain lncRNAs were associated with oxaliplatin sensitivity and predicted the response to oxaliplatin treatment. The prognostic models established based on the key lncRNAs could predict the prognosis of patients given oxaliplatin-based chemotherapy.

Identification and validation of a cellular senescence-related lncRNA signature for prognostic prediction in patients with multiple myeloma

ABSTRACT Multiple myeloma (MM) is the second most common hematologic malignancy, which primarily occurs in the elderly. Cellular senescence is considered to be closely associated with the occurrence and progression of malignant tumors including MM, and lncRNA can mediate the process of cellular senescence by regulating key signaling pathways such as p53/p21 and p16/RB. However, the role of cellular senescence related lncRNAs (CSRLs) in MM development has never been reported. Herein, we identified 11 CSRLs (AC004918.5, AC103858.1, AC245100.4, ACBD3-AS1, AL441992.2, ATP2A1-AS1, CCDC18-AS1, LINC00996, TMEM161B-AS1, RP11-706O15.1, and SMURF2P1) to build the CSRLs risk model, which was confirmed to be highly associated with overall survival (OS) of MM patients. We further demonstrated the strong prognostic value of the risk model in MM patients receiving different regimens, especially for those with three-drug combination of bortezomib, lenalidomide, and dexamethasone (VRd) as first-line therapy. Not only that, our risk model also excels in predicting the OS of MM patients at 1, 2, and 3 years. In order to verify the function of these CSRLs in MM, we selected the lncRNA ATP2A1-AS1 which presented the largest expression difference between high-risk groups and low-risk groups for subsequent analysis and validation. Finally, we found that down-regulation of ATP2A1-AS1 can promote cellular senescence in MM cell lines. In conclusion, the CSRLs risk model established in present study provides a novel and more accurate method for predicting MM patients’ prognosis and identifies a new target for MM therapeutic intervention.

Comprehensive analysis of oxidative stress-related lncRNA signatures in glioma reveals the discrepancy of prognostic and immune infiltration

Oxidative stress refers to the process of reactive oxide species (ROS) increase in human body due to various factors, which leads to oxidative damage in human tissues. Current studies have confirmed that sustained oxidative stress is one of the distinctive features throughout the development of tumors. Numerous reports have shown that lncRNAs can regulate the process of oxidative stress through multiple pathways. However, the relationship between glioma-associated oxidative stress and lncRNAs is not clearly investigated. RNA sequencing data of GBM (glioblastoma) and LGG (low grade glioma) and corresponding clinical data were retrieved from the TCGA database. Oxidative stress related lncRNAs (ORLs) were identified by Pearson correlation analysis. Prognostic models for 6-ORLs were structured in the training cohort by univariate Cox regression analysis, multivariate Cox regression analysis and LASSO regression analysis. We constructed the nomogram and verified its predictive efficacy by Calibration curves and DCA decision curves. The biological functions and pathways of 6-ORLs-related mRNAs were inferred by Gene Set Enrichment Analysis. Immune cell abundance and immune function associated with risk score (RS) were estimated by ssGSEA, CIBERSORT and MCPcounter synthetically. External validation of the signature was completed using the CGGA-325 and CGGA-693 datasets. 6-ORLs signature—AC083864.2, AC107294.1, AL035446.1, CRNDE, LINC02600, and SNAI3-AS1—were identified through our analysis as being predictive of glioma prognosis. Kaplan–Meier and ROC curves indicated that the signature has a dependable predictive efficacy in the TCGA training cohort, validation cohort and CGGA-325/CGGA-693 test cohort. The 6-ORLs signature were verified to be independent prognostic predictors by multivariate cox regression and stratified survival analysis. Nomogram built with risk scores had strong predictive efficacy for patients' overall survival (OS). The outcomes of the functional enrichment analysis revealing potential molecular regulatory mechanisms for the 6-ORLs. Patients in the high-risk subgroup presented a significant immune microenvironment of macrophage M0 and cancer-associated fibroblast infiltration which was associated with a poorer prognosis. Finally, the expression levels of 6-ORLs in U87/U251/T98/U138 and HA1800 cell lines were verified by RT-qPCR. The nomogram in this study has been made available as a web version for clinicians. This 6-ORLs risk signature has the capabilities to predict the prognosis of glioma patients, assist in evaluating immune infiltration, and assess the efficacy of various anti-tumor systemic therapy regimens.

RGN as a prognostic biomarker with immune infiltration and ceRNA in lung squamous cell carcinoma

Regucalcin (RGN) is a potent inhibitory protein of calcium signaling and expresses in various tissues. However, the role of RGN in the tumor immunological microenvironment in lung squamous cell carcinoma (LUSC) remains unclear. This study identified the expression of RGN from public databases and immunohistochemistry with clinical specimen. The association between RGN and the tumor immune microenvironment (TIME) was investigated in LUSC by ESTIMATE and CIBERSORT algorithms. Similarly, the Tumor IMmune Estimation Resource (TIMER) database was used to identify the correlation between RGN and immune cells. The ceRNA network was established based on the data obtained from public databases. Finally, prediction of drug response to chemotherapy and immunotherapy was performed to evaluate clinical significance. This study found that RGN expression was significantly downregulated in tumor tissues and closely related to clinical factors and prognosis of LUSC patients. Differentially expressed genes (DEGs) grouped by the expression of RGN were mostly involved in immunobiological processes such as humoral immune response and leukocyte mediated immunity. RGN and its related miRNA (has-miR-203a-3p) and lncRNAs (ZNF876P and PSMG3-AS1) constructed the novel prognosis-related ceRNA network. Plasma cells, T cells CD4 memory resting, Macrophages M0, Macrophages M1, Mast cells resting, Mast cells activated and Neutrophils showed significantly different levels of infiltration between high and low RGN expression groups. The TIMER database showed that RGN expression was positively correlated with certain immune infiltrating cells. High RGN expression group showed a higher TIDE score, a higher dysfunction score and a lower MSI score, presenting a possible lower efficacy after accepting the immunotherapy than low RGN expression group. RGN expression was closely associated with prognosis of LUSC patients and played an important role in tumor microenvironment. This suggests that RGN could be a promising biomarker for assessing immunotherapy efficacy and prognosis.

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.

Expression analysis of cell cycle related lncRNAs in breast cancer tissues.

Cell cycle regulation is an important cellular function. Abnormal regulation of this process can cause cancer. Several genes are involved in this process. There is no comprehensive study on expression pattern of cell cycle related lncRNAs in breast cancer patients. In the current study, we evaluated expressions of LINC00668, PRDM16-DT, SNHG7 and CDKN2A in 42 pairs of breast cancer tissues and adjacent non-tumoral tissues. Expression of SNHG7 was significantly lower in tumoral tissues compared with non-tumoral tissues. However, expressions of LINC00668, PRDM16-DT and CDKN2A were not significantly different between these two sets of samples. Expression levels of SNHG7 could separate tumoral tissues from non-tumoral tissues with AUC value=0.66, sensitivity=61% and specificity=73%. Expression of CDKN2A was associated with clinical stage (P value=0.01). Expression levels of LINC00668, PRDM16-DT, SNHG7 and CDKN2A were higher in estrogen receptor (ER) positive samples compared with ER negative ones (P values=0.044, 0.008, 0.002 and 0.022, respectively). Moreover, expression of SNHG7 was higher in progesterone receptor (PR) positive samples compared with PR negative ones (P value=0.02). Finally, expressions of PRDM16-DT, SNHG7 and CDKN2A were higher in HER2/neu positive samples compared with HER2/neu negative ones (P values=0.017, 0.02 and 0.021, respectively). Taken together, our study demonstrates possible roles of these genes in breast cancer and warrants further functional studies.

The pluripotent factor OCT4A enhances the self-renewal of human dental pulp stem cells by targeting lncRNA FTX in an LPS-induced inflammatory microenvironment

BackgroundRegulating the pluripotency of human dental pulp stem cells (hDPSCs) is key for the self-repair of injured dental pulp. We previously found that OCT4A promotes the proliferation and odontogenic differentiation of human dental pulp cells (hDPCs). Recent studies have shown the interaction between OCT4A and lncRNAs in pluripotency maintenance of various stem cells. The aim of this study was to explore the underlying roles and mechanisms of OCT4A and its related lncRNAs in the proliferation and multidirectional differentiation of hDPSCs in an inflammatory microenvironment.MethodsHuman lncRNA microarrays were applied to screen out the differentially expressed lncRNAs in hDPSCs between the OCT4A-overexpressing and vector groups. Lipopolysaccharide (LPS) was used to simulate the inflammatory microenvironment. The effects of OCT4A and the lncRNA FTX on the proliferation and multidifferentiation of hDPSCs were observed by the CCK-8 assay, EdU staining, real-time PCR, western blotting, and Alizarin red and oil red O staining. Bioinformatics analysis and chromatin immunoprecipitation (ChIP) assays were performed to clarify the targeted mechanism of OCT4A on FTX. The regulation by FTX of the expression of OCT4A and its downstream pluripotent transcription factors SOX2 and c-MYC was further detected by real-time PCR and western blotting.ResultsThe microarray results showed that 978 lncRNAs (250 of which were upregulated and 728 downregulated) were potentially differentially expressed genes (fold change ≥ 2, P < 0.05). LPS stimulation attenuated the self-renewal of hDPSCs. OCT4A enhanced the cell proliferation and multidifferentiation capacities of hDPSCs in an inflammatory microenvironment, while FTX exhibited the opposite effects. OCT4A negatively regulated FTX function by binding to specific regions on the FTX promoter, thereby inhibiting the transcription of FTX. Moreover, overexpression of FTX downregulated the expression of OCT4A, SOX2 and c-MYC, whereas knockdown of FTX facilitated their expression.ConclusionsOCT4A was found to be a crucial factor maintaining the self-renewal of hDPSCs by transcriptionally targeting FTX in an inflammatory microenvironment. Moreover, we proposed a novel function of FTX in negatively regulating the pluripotency and multilineage differentiation capacity of hDPSCs. The hierarchical organization between OCT4A and FTX expanded the understanding of the network between transcription factors and lncRNAs in fine-tuning the pluripotency/differentiation balance of adult stem cells, and provided prospective targets for optimizing dental-derived stem cell sources for regenerative endodontics.

Identification of immune and Toll-like receptor signaling pathway related feature lncRNAs to construct diagnostic nomograms for acute ischemic stroke

We aimed to identify the immune and Toll-like receptor (TLR) signaling pathway related feature lncRNAs to construct the diagnostic nomograms for acute ischemic stroke (AIS). Two AIS-associated expression profiles GSE16561 and GSE22255 were downloaded from NCBI Gene Expression Omnibus, the former was the training set and the latter was the validation set. The differential expression genes (DEGs) and lncRNAs (DElncRNAs) related to TLR signaling pathway were identified between AIS and control groups. The single sample gene set enrichment analysis (ssGSEA) was applied to evaluate the immune infiltration. The immune and TLR signaling pathway related DElncRNAs were determined. Three optimization algorithms were utilized to select the immune and TLR signaling pathway related feature lncRNAs to construct the diagnostic nomograms of AIS. Based on the lncRNA signature, a ceRNA network was constructed. 37 DEGs and 28 DElncRNAs related to TLR signaling pathway were identified in GSE16561. 16 immune cell types exhibited significant differences in distribution between AIS and control groups. 28 immune and TLR signaling pathway related DElncRNAs were determined. 8 immune and TLR signaling pathway related feature lncRNAs were selected. The diagnostic nomograms of AIS performed well in both datasets. A ceRNA network was constructed consisting of 7 immune and TLR signaling pathway related feature lncRNAs as well as 19 AIS related miRNAs and 21 TLR signaling pathway related genes. LINC00173, LINC01089, LINC02210, MIR600HG, SNHG14, TP73-AS1, LINC00680 and CASC2 may be the potential biomarkers of AIS diagnosis, and TLR signaling pathway may be a promising immune related therapeutic target for AIS.

Machine learning-based construction of a ferroptosis and necroptosis associated lncRNA signature for predicting prognosis and immunotherapy response in hepatocellular cancer.

Liver hepatocellular carcinoma (LIHC), one of the most common malignancies worldwide, occurs with high incidence and mortality. Ferroptosis and necroptosis are critically associated with LIHC prognosis. Some long non-coding RNAs (lncRNAs) have been found to induce ferroptosis and necroptosis in hepatocellular carcinoma cells. Cox regression analysis was used to construct a risk model for LIHC based on differentially expressed ferroptosis and necroptosis related lncRNAs (F-NLRs), and their expression in SMMC7721, HepG2 and WRL68 cells was detected by qPCR. Five F-NLRs were associated with LIHC prognosis, including KDM4A-AS1, ZFPM2-AS1, AC099850.3, MKLN1-AS, and BACE1-AS. Kaplan-Meier survival analysis indicated that patients with LIHC in the high-risk group were associated with poor prognosis. The combined F-NLR signature model demonstrated a prognostic AUC value of 0.789 and was more accurate than standard clinical variables for predicting LIHC prognosis. T cell functions and immunotherapy responses differed significantly between patients in the low- and high-risk groups. Additionally, immune checkpoints and m6A-related genes were differentially expressed between patients in the two risk groups. Furthermore, proteins encoded by the five F-NLRs were overexpressed in four liver cancer cell lines compared to that in human liver cell line WRL68. Pan-cancer examination revealed that expression levels of the five F-NLRs differed between most common tumor types and normal tissues. F-NLRs identified in this study provide a predictive signature representing ferroptosis and necroptosis in LIHC, which correlated well with patient prognosis, clinicopathological characteristics, and immunotherapy responses. The study findings help to elucidate the mechanisms of F-NLRs in LIHC and provide further guidance for the selection and development of immunotherapeutic agents for LIHC.

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.

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.

Prognostic signature construction and immunotherapy response analysis for Uterine Corpus Endometrial Carcinoma based on cuproptosis-related lncRNAs

BackgroundAs a general female malignant tumor, Uterine Corpus Endometrial Carcinoma (UCEC) has high mortality and relapses. Cuproptosis was found to play an essential role in tumor by more and more researches. However, it is still unclear of the prognostic value and function of cuproptosis related Long non-coding RNA (lncRNA) in UCEC. MethodsSequencing data with the corresponding clinical data and cuproptosis-related genes (CRGs) data were obtained from the Cancer Gene Atlas (TCGA) database and cuproptosis related studies. Pearson test was applied to select cuproptosis-related lncRNAs (CRLs). Prognosis associated CRLs was identified by univariate Cox analysis and the predictors were determined by least absolute shrinkage and selection operator (Lasso)-Cox and multivariate Cox analyses to construct the cuproptosis-related lncRNA prognostic signature (CRLPS). The performance of the CRLPs was evaluated by consistency index (C-index) and Kaplan-Meier analysis. A nomogram model was constructed for survival prediction and the accuracy of the model was evaluated by calibration curve. Finally, immune related analyses were applied to predict immune responses and identify drugs with potential efficacy for the overall survival (OS). ResultsA total of 734 CRLs were found and 29 of them were identified as prognosis related lncRNAs. 12 CRLs were finally determined to build the CRLPS which revealed good ability on prognosis predicting. Subsequently, risk score of the CRLPS and grade were assessed as independent prognosis factors for UCEC, based on which the prognostic model provided the highest prediction accuracy of 99.7%. The calibration curve suggested that the prediction results consisted well with the observation. Enrichment analysis showed the CRLPS was mainly associated with tumor development and immune response. Patients in low tumor mutation burden (TMB) group had poorer OS. Significant difference was found in tumor immune dysfunction and exclusion (TIDE) score between different risk score groups. Finally, based on the CRLPs, drug sensitivity analysis identified nine anticancer drugs with potential efficacy on prognosis. ConclusionCuproptosis-related lncRNA prognostic signature was constructed for UCEC for the first time. Its high reliability and accuracy on predicting prognosis and immunotherapy response provided new perspective to explore the tumor mechanism and improve clinical prognosis. Nine discovered sensitive drugs provided important clues for personalized treatment of UCEC.

A novel oxidative-stress related lncRNA signature predicts the prognosis of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a primary malignant tumour of tubular epithelial origin and is most common in the urinary tract. Growing evidence suggests that oxidative stress (OS), generates high levels of reactive oxygen species (ROS) and free radicals, and plays a critical role in cancer in humans. However, the predictive value of OS-related long non-coding RNAs (lncRNAs) in ccRCC remains unclear. We constructed a predictive signature of survival based on OS-related lncRNAs that were obtained from The Cancer Genome Atlas (TCGA–KIRC), to predict the prognosis of patients with ccRCC. The signature comprised seven lncRNAs: SPART-AS1, AL162586.1, LINC00944, LINC01550, HOXB-AS4, LINC02027, and DOCK9-DT. OS-related signature of lncRNAs had diagnostic efficiency higher than that of clinicopathological variables, with an area of 0.794 under the receiver operating characteristic curve. Additionally, the nomogram based on risk scores and clinicopathological variables (age, gender, grade, stage, M-stage, and N-stage) showed strong predictive performance. Patients with high-risk were found to be more sensitive to the therapeutic drugs ABT.888, AICAR, MS.275, sunitinib, AZD.2281, and GDC.0449. Our constructed the predictive signature can independently predict the prognosis of patients with ccRCC; however, the underlying mechanism needs further investigation.