Prognostic lncRNA Signature Research Articles

Construction of an endoplasmic reticulum stress and cuproptosis -related lncRNAs signature in chemosensitivity in hepatocellular carcinoma by comprehensive bioinformatics analysis

Endoplasmic reticulum stress (ERS) and cuproptosis have remarkable effects on hepatocellular carcinoma (HCC) leading to a poor prognosis. The current study aimed to explore credible signature for predicting the prognosis of HCC based on ERS and cuproptosis-related lncRNAs. In our study, clinical and transcriptomic profiles of HCC patients were obtained from the Cancer Genome Atlas (TCGA) database. An ERS and cuproptosis-related lncRNA prognostic signature, including NRAV, SNHG3, LINC00839 and AC004687.1, was determined by correlation tests, Cox regression analysis, least absolute shrinkage, and selection operator (LASSO) methods. Survival and predictive value were evaluated using Kaplan–Meier and receiver operating characteristic (ROC) curves, while calibration and nomograms curves were developed. Besides the enrichment analyses for ERS and cuproptosis-related lncRNAs, mutational status and immune status were assessed with TMB and ESTIMATE. Additionally, consensus cluster analysis was employed to compare cancer subtype differences, while drug sensitivity and immunologic efficacy were evaluated for further exploration. qRT-PCR and CCK-8 were utilized to verify the alteration of the prognostic lncRNAs expression and proliferation in vitro. High-risk groups exhibited poorer prognosis. The signature exhibited robust predictive value as an independent prognostic indicator and showed significant correlation with clinicopathological features. In the enriched analysis, biological membrane pathways were enriched. Low-risk patients had lower TMB and higher immune status. A cluster analysis revealed that cluster 2 had the best clinical immunological efficacy and most active immune function. In brief, our constructed signature with ERS and cuproptosis-related lncRNAs was associated survival outcomes of HCC, and can be used to predict the clinical classification and curative effect.

Immunogenomic profiles and therapeutic options of the pan-programmed cell death-related lncRNA signature for patients with bladder cancer

Programmed cell death (PCD) is a process that eliminates infected, damaged, or possibly neoplastic cells to sustain homeostatic multicellular organisms. Although long noncoding RNAs (lncRNAs) are involved in various types of PCD and regulate tumor growth, invasion, and migration, the role of PCD-related lncRNAs in bladder cancer still lacks systematic exploration. In this research, we integrated multiple types of PCD as pan-PCD and identified eight pan-PCD-related lncRNAs (LINC00174, HCP5, HCG27, UCA1, SNHG15, GHRLOS, CYB561D2, and AGAP11). Then, we generated a pan-PCD-related lncRNA prognostic signature (PPlncPS) with excellent predictive power and reliability, which performed equally well in the E-MTAB-4321 cohort. In comparison with the low-PPlncPS score group, the high-PPlncPS score group had remarkably higher levels of angiogenesis, matrix, cancer-associated fibroblasts, myeloid cell traffic, and protumor cytokine signatures. In addition, the low-PPlncPS score group was positively correlated with relatively abundant immune cell infiltration, upregulated expression levels of immune checkpoints, and high tumor mutation burden (TMB). Immunogenomic profiles revealed that patients with both low PPlncPS scores and high TMB had the best prognosis and may benefit from immune checkpoint inhibitors. Furthermore, for patients with high PPlncPS scores, docetaxel, staurosporine, and luminespib were screened as potential therapeutic candidates. In conclusion, we generated a pan-PCD-related lncRNA signature, providing precise and individualized prediction for clinical prognosis and some new insights into chemotherapy and immune checkpoint inhibitor therapy for bladder cancer.

Development of a disulfidptosis-related lncRNA prognostic signature for enhanced prognostic assessment and therapeutic strategies in lung squamous cell carcinoma

Limited treatment options and poor prognosis present significant challenges in the treatment of lung squamous cell carcinoma (LUSC). Disulfidptosis impacts cancer progression and prognosis. We developed a prognostic signature using disulfidptosis-related long non-coding RNAs (lncRNAs) to predict the prognosis of LUSC patients. Gene expression matrices and clinical information for LUSC were downloaded from the TCGA database. Co-expression analysis identified 209 disulfidptosis-related lncRNAs. LASSO-Cox regression analysis identified nine key lncRNAs, forming the basis for establishing a prognostic model. The model’s validity was confirmed by Kaplan–Meier and ROC curves. Cox regression analysis identified the risk score (RS) as an independent prognostic factor inversely correlated with overall survival. A nomogram based on the RS demonstrated good predictive performance for LUSC patient prognosis. The relationship between RS and immune function was explored using ESTIMATE, CIBERSORT, and ssGSEA algorithms. According to the TIDE database, a negative correlation was found between RS and immune therapy responsiveness. The GDSC database revealed that 49 drugs were beneficial for the low-risk group and 25 drugs for the high-risk group. Silencing C10orf55 expression in SW900 cells reduced invasiveness and migration potential. In summary, this lncRNA model based on TCGA-LUSC data effectively predicts prognosis and assists clinical decision-making.

Identification of a novel cellular senescence-related lncRNA signature for prognosis and immune response in osteosarcoma.

Cellular senescence, a novel hallmark of cancer, is associated with patient outcomes and tumor immunotherapy. However, at present, there is no systematic study on the use of cellular senescence-related long non-coding RNAs (CSR-lncRNAs) to predict survival in patients with osteosarcoma. In this study, we aimed to identify a CSR-lncRNAs signature and to evaluate its potential use as a survival prognostic marker and predictive tool for immune response of osteosarcoma. We downloaded a cohort of patients with osteosarcoma from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We performed differential expression and co-expression analyses to identify CSR-lncRNAs. We performed univariate and multivariate Cox regression analyses along with the random forest algorithm to identify lncRNAs significantly correlated with senescence. Subsequently, we assessed the predictive models using survival curves, receiver operating characteristic curves, nomograms, C-index, and decision curve analysis. Based on this model, patients with osteosarcoma were divided into two groups according to their risk scores. Then, using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses, we compared their clinical characteristics to uncover functional differences. We further conducted immune infiltration analyses using estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE), cell-type identification by estimating relative subsets of rna transcripts (CIBERSORT), and single-sample gene set enrichment analysis for the two groups. We also evaluated the expression of the target genes of immune checkpoint inhibitors (ICIs). We identified six lncRNAs that were significantly correlated with senescence and accordingly established a novel cellular senescence-related lncRNA prognostic signature incorporating these lncRNAs. The nomogram indicated that the risk model was an independent prognostic factor that could predict the survival of patients with osteosarcoma. This model demonstrated high accuracy upon validation. Further analysis revealed that patients with osteosarcoma in the low-risk group exhibited better clinical outcomes and enhanced immune infiltration. The six-CSR-lncRNA prognostic signature effectively predicted survival outcomes and patients in the low-risk group might have improved immune infiltration.

Characterization of an endoplasmic reticulum stress-associated lncRNA prognostic signature and the tumor-suppressive role of RP11-295G20.2 knockdown in lung adenocarcinoma

Endoplasmic reticulum stress (ERS) is commonly induced by accumulating misfolded or unfolded proteins in tumor microenvironment. Long non-coding RNAs (lncRNAs) play important roles in ERS response and lung adenocarcinoma (LUAD) progression. However, the role of ERS-related lncRNAs in LUAD remains unknown. In this study, we aimed to identify ERS-associated lncRNAs with prognostic value in LUAD and characterize their clinical implications. Cox and least absolute shrinkage and selection operator regression analyses identified nine ERS-related lncRNAs with independent prognostic abilities, including five protective factors (CROCCP2, KIAA0125, LINC0996, RPARP-AS1 and TBX5-AS1) and four risk factors (LINC0857, LINC116, RP11-21L23.2 and RP11-295G20.2). We developed an ERS-related lncRNA risk prediction model in predicting overall survival of LUAD patients, which classified TCGA cohorts into high-risk (HS) and low-risk (LS) groups. Comprehensive bioinformatic analyses revealed HS patients featured with late-stage tumors, greater mutation burdens, weaker anti-tumor immunity/responses, and lower sensitivity to targeted drugs compared to LS patients, contributing to tumor progression and a poor prognosis. Functional enrichment analysis implicated these ERS-related lncRNAs in cell migration, cell death, and immunity. Furthermore, expression of the most significantly upregulated risk lncRNA, RP11-295G20.2, was validated at the mRNA level using clinical LUAD samples. Knockdown of RP11-295G20.2 obviously reduced ERS and suppressed proliferation, invasion, and migration of LUAD cells. This novel ERS-related lncRNA signature provides a new biomarker for prognostic prediction, and ERS-associated RP11-295G20.2 serves as a potential therapeutic target in LUAD.

Identification and Verification of a Glycolysis-Related lncRNA Prognostic Signature for Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a primary liver cancer with a high mortality rate. The search for a new biomarker could help the prognosis of HCC patients. We identified the glycolytic gene set associated with HCC and the glycolytic lncRNA based on TCGA and MsigDB databases. According to these lncRNAs, K-means clustering, and regression analysis were performed on the patients. Two groups of HCC patients with different lncRNA expression levels were obtained based on K-means clustering results. The results of difference analysis and enrichment analysis showed that DEmRNA in the two HCC populations with significant survival differences was mainly enriched in transmembrane transporter complex, RNA polymerase II specificity, cAMP signaling pathway, and calcium signaling pathway. In addition, a prognostic model of HCC with 4 DElncRNAs was constructed based on regression analysis. ROC curve analysis showed that the model had good predictive performance. Drug predictionresults showed that the efficacy of JQ1, niraparib, and teniposide was higher in the low-risk group than in the high-risk group. In conclusion, this study preliminarily identified glycolytic-related prognostic features of lncRNAs in HCC and constructed a risk assessment model. The results of this study are expected to guide the prognosis assessment of clinical HCC patients.

Identification of a novel prognostic cuproptosis-associated LncRNA signature for predicting prognosis and immunotherapy response in patients with esophageal cancer

Nowadays, effective prognostic models for esophageal cancer (ESCA) are still lacking. Long noncoding RNAs (lncRNAs) are commonly utilized as indicators for diagnosing cancer and forecasting patient outcomes. Cuproptosis is regulated by multiple genes and is crucial to the progression of ESCA. However, it is not yet clear what role the cuproptosis-associated lncRNAs (CuALs) play in ESCA. To tackle this problem, a prognostic signature incorporating three CuALs was created. This signature was constructed by the use of the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression. Subsequently, the signature effectively stratified ESCA samples into a high-risk group and a low-risk group. Those in the low-risk group demonstrated extended overall survival (OS), as well as increased infiltration of T cells, macrophages, and NK cells, suggesting a potentially enhanced response to immunotherapy. The ROC curve analysis demonstrated that this prognostic signature outperformed conventional clinical factors in predicting patient prognosis (AUC = 0.708). K-M survival analysis and correlation analysis identified UGDH-AS1 (a CuAL) as a protective factor positively associated with patient prognosis. The results of RT-qPCR and wound healing assays indicated that UGDH-AS1 is overexpressed in ESCA and could inhibit cancer cell migration. In general, the prognostic signature of CuALs demonstrated a robust capability in forecasting the immune environment and patient prognosis, highlighting its potential as a tool for enhancing personalized treatment strategies in ESCA.

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 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.

LncRNAs expression profile in a family household cluster of COVID-19 patients.

More than 3 years after the start of SARS-CoV-2 pandemic, the molecular mechanisms behind the viral pathogenesis are still not completely understood. Long non-coding RNAs (lncRNAs), well-known players in viral infections, can represent prime candidates for patients' risk stratification. The purpose of the current study was to investigate the lncRNA profile in a family cluster of COVID-19 cases with different disease progression, during the initial wave of the pandemic and to evaluate their potential as biomarkers for COVID-19 evolution. LncRNA expression was investigated in nasopharyngeal swabs routinely collected for diagnosis. Distinct expression patterns of five lncRNAs (HOTAIR, HOTAIRM1, TMEVPG1, NDM29 and snaR) were identified in all the investigated cases, and they were associated with disease severity. Additionally, a significant increase in the expression of GAS5-family and ZFAS1 lncRNAs, which target factors involved in the inflammatory response, was observed in the sample collected from the patient with the most severe disease progression. An lncRNA prognostic signature was defined, opening up novel research avenues in understanding the interactions between lncRNAs and SARS-CoV-2.

Development and validation of a novel prognostic lncRNA signature based on the APOBEC3 family genes in gastric cancer

IntroductionGastric Cancer (GC) refers to a prevalent malignant cancer accompanied by a weak prognosis. The APOBEC3 family genes and lncRNAs are linked with cancer progression. Nevertheless, there is still a scarcity of data concerning the prognostic value of APOBEC3-related lncRNAs in GC. MethodsWe extracted the data from GC samples, including transcriptome as well as clinical data, obtained from the TCGA database. Then, we screened for lncRNAs that were correlated with the APOBEC3 family genes and constructed an APOBEC3-related lncRNA prognostic signature (LPS) by utilizing univariate Cox and lasso regression analysis. Furthermore, we validated our constructed signature and evaluated it thoroughly, including analysis of its function, immunity, mutations, and clinical applications via multiple methods, including Metascape, GSEA, and analyses including TIC and TME, immune checkpoints, CNV and SNPs, Kaplan-Meier survival curves, nomogram, decision tree and drug prediction analysis. Finally, we overexpressed LINC01094 to evaluate the impacts on the proliferation as well as migration with regards to KATO-2 cells. ResultsWe selected eight lncRNAs for our APOBEC3-related LPS, which is demonstrated as a valuable tool in predicting the individual GC patients’ prognosis. Subsequently, we segregated the samples into subgroups of high-as well as low-risk relying on the risk score with regards to APOBEC3-related LPS. By performing functional analysis, we have shown that immune-as well as tumor-related pathways were enriched in high- and low-risk GC patients. Furthermore, immune analysis revealed a robust correlation between the APOBEC3-related LPS and immunity. We found that immune checkpoints were significantly associated with the APOBEC3-related LPS and were greatly exhibited in GC tumor and high-risk samples. Mutational analysis suggested that the mutational rate was greater in low-risk samples. Furthermore, we predicted small molecular drugs displayed greater sensitivity in patients categorized as high-risk. Moreover, the immune response was also better in high-risk patients. Of these drugs, dasatinib was significant in both methods and might be considered a potential novel drug for treating high-risk GC patients. Finally, we found that LINC01094 has the potential to enhance the migration, proliferation as well as inhibit apoptosis of KATO-2 in GC cells. And Dasatinib has an inhibitory effect on the migration as well as proliferation in GC cells. ConclusionWe created a novel APOBEC3-related LPS in predicting the prognosis with regards to individual GC patients. Importantly, this APOBEC3-related LPS was closely associated with immunity and might guide clinical treatment.

ALMS1-IT1: A Key Player in the Novel Disulfidptosis-Related LncRNA Prognostic Signature for Head and Neck Squamous Cell Carcinoma.

Disulfidptosis is a newly discovered form of programmed cell death that is induced by disulfide stress. It is closely associated with various cancers, including head and neck squamous cell carcinoma (HNSCC). However, the factors involved in the modulation of disulfidptosis-related genes (DRGs) still remain unknown. In this study, we established and validated a novel risk score model composed of 11 disulfidptosis-related lncRNAs (DRLs) based on 24 DRGs in HNSCC. The results revealed strong correlations between the 11-DRL prognostic signature and clinicopathological features, immune cell infiltration, immune-related functions, and disulfidptosis-associated pathways, including NADPH and disulfide oxidoreductase activities. Furthermore, we studied and verified the involvement of ALMS1-IT1, one of the 11 model DRLs, in the disulfidptosis of HNSCC cell lines. A series of assays demonstrated that ALMS1-IT1 modulated cell death under starvation conditions in a pentose phosphate pathway (PPP)-dependent manner. Knockdown of ALMS1-IT1 inhibited the PPP, contributing to a decline in NADPH levels, which resulted in the formation of multiple intermolecular disulfide bonds between actin cytoskeleton proteins and the collapse of F-actin in the cytoplasm. Therefore, ALMS1-IT1, which is highly expressed in SLC7A11high cells, can be considered a promising therapeutic target for disulfidptosis-focused treatment strategies for cancer and other diseases.

Construction and validation of a prognostic signature for mucinous colonic adenocarcinoma based on N7-methylguanosine-related long non-coding RNAs.

Mucinous colonic adenocarcinoma remains a challenging disease due to its high propensity for metastasis and recurrence. N7-methylguanosine (m7G) and long non-coding RNA (lncRNA) are closely associated with the occurrence and progression of tumors. However, research on m7G-related lncRNA in mucinous colonic adenocarcinoma is lacking. Therefore, we sought to explore the prognostic impact of m7G-related lncRNAs in mucinous adenocarcinoma (MC) patients. In this study, Pearson analysis was used to identify m7G-related lncRNAs from transcriptome data in The Cancer Genome Atlas (TCGA). Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression were used to further screen m7G-related lncRNAs and incorporate them into a prognostic signature. Based on the risk model, patients were divided into low- and high-risk groups and randomly assigned to the training set and test sets in a 6:4 ratio. Kaplan-Meier, receiver operating characteristic (ROC) curve, multivariate regression, and nomogram analyses were used to confirm the accuracy of the signature. The CIBERSORT algorithm was used to calculate the degree of immune cell infiltration (ICI). Finally, the correlation of the prognostic signature with tumor mutational burden (TMB) and immunophenotype score (IPS) was evaluated. A total of 432 m7G-related lncRNAs were identified by Pearson analysis. Univariate Cox regression, LASSO regression and survival analysis were performed to further select six m7G-related lncRNAs (P<0.05): AC254629.1, LINC01133, LINC01134, MHENCR, SMIM2-AS1, and XACT. Based on the risk model, heat maps, Kaplan-Meier curves, and ROC curves were constructed, and the results showed that there were significant differences in expression levels and survival status between the two risk groups. The area under the ROC curve (AUC) values for 3-, 5-, and 10-year survival in the training set were 0.944, 0.957, and 1.000, respectively. And in the test set were 0.964, 1.000, and 1.000, respectively. Subsequently, univariate and multivariate regression analyses of clinical characteristics and risk score were performed. The results of risk score were [hazard ratio (HR): 6.458, 95% confidence interval (CI): 2.708-15.403, P<0.001; HR: 7.280, 95% CI: 2.500-21.203, P<0.001], respectively. Using the risk score as an independent prognostic factor, the AUC of it over 3, 5, and 10 years was 0.911, 0.955, and 0.961, respectively. Calibration plots for the nomogram show that the model calibration line is very close to the ideal calibration line, indicating good calibration. The level of ICI was significantly different in the different risk groups. Survival analysis showed that, regardless of TMB risk, patients with MC and a high-risk score consistently had a poor overall survival (OS). The m7G-related lncRNA prognostic signature has potential value for the prognosis of mucinous colonic adenocarcinoma.

Development of a prognostic Neutrophil Extracellular Traps related lncRNA signature for soft tissue sarcoma using machine learning.

Soft tissue sarcoma (STS) is a highly heterogeneous musculoskeletal tumor with a significant impact on human health due to its high incidence and malignancy. Long non-coding RNA (lncRNA) and Neutrophil Extracellular Traps (NETs) have crucial roles in tumors. Herein, we aimed to develop a novel NETsLnc-related signature using machine learning algorithms for clinical decision-making in STS. We applied 96 combined frameworks based on 10 different machine learning algorithms to develop a consensus signature for prognosis and therapy response prediction. Clinical characteristics, univariate and multivariate analysis, and receiver operating characteristic curve (ROC) analysis were used to evaluate the predictive performance of our models. Additionally, we explored the biological behavior, genomic patterns, and immune landscape of distinct NETsLnc groups. For patients with different NETsLnc scores, we provided information on immunotherapy responses, chemotherapy, and potential therapeutic agents to enhance the precision medicine of STS. Finally, the gene expression was validated through real-time quantitative PCR (RT-qPCR). Using the weighted gene co-expression network analysis (WGCNA) algorithm, we identified NETsLncs. Subsequently, we constructed a prognostic NETsLnc signature with the highest mean c-index by combining machine learning algorithms. The NETsLnc-related features showed excellent and stable performance for survival prediction in STS. Patients in the low NETsLnc group, associated with improved prognosis, exhibited enhanced immune activity, immune infiltration, and tended toward an immunothermal phenotype with a potential immunotherapy response. Conversely, patients with a high NETsLnc score showed more frequent genomic alterations and demonstrated a better response to vincristine treatment. Furthermore, RT-qPCR confirmed abnormal expression of several signature lncRNAs in STS. In conclusion, the NETsLnc signature shows promise as a powerful approach for predicting the prognosis of STS. which not only deepens our understanding of STS but also opens avenues for more targeted and effective treatment strategies.

Liquid-liquid phase separation-related lncRNA prognostic signature and ZNF32-AS2 as a novel biomarker in hepatocellular carcinoma

BackgroundLiquid-liquid phase separation (LLPS) enhances oncogenic signaling pathways and advances cancer progression, and has been proposed as a promising cancer biomarker and intervention target. Nevertheless, doubts remain about the prognostic importance of LLPS-related long non-coding RNAs (lncRNAs) in hepatocellular carcinoma (HCC). Methods: An LLPS-related lncRNA prognostic signature was generated by drivers and regulators of LLPS, and was validated in external datasets. The underlying genetic changes and functional enrichment of the signature were assessed. The drug sensitivity and response to immunotherapy were predicted in patients categorized as high-risk and low-risk. Clinical samples, phase separation agonist, and dispersant were used to identify lncRNAs with the most significant expression change. Cancer cells with ZNF32-AS2 expression regulation were subjected to colony formation assay, scratch test assay, migration and invasion assay, sorafenib resistance assay, and xenograft tumor model. Results: The signature of LLPS-related hub lncRNAs identified through Weighted Gene Co-Expression Network Analysis showed outstanding performance in training and external validation cohorts consistently, and the molecular characteristics varied between different risk groups. Potential drugs for high-risk individuals were identified, and low-risk individuals demonstrated a more favorable reaction to immunotherapy. ZNF32-AS2 showed the most significant expression change in phase separation agonist and dispersant treatment. ZNF32-AS2 promoted the proliferation, mobility, and sorafenib resistance of liver cancer cells. Conclusions: The LLPS-related lncRNA signature may help assess prognosis and predict treatment efficacy in clinical settings. LLPS-related ZNF32-AS2 promoted the proliferation, mobility, and sorafenib resistance of liver cancer cells, and may be a novel potential biomarker in hepatocellular carcinoma.

Ferroptosis-associated lncRNA prognostic signature predicts prognosis and immune response in laryngeal squamous carcinoma.

In order to construct a prognostic model of ferroptosis-related lncRNA associated with laryngeal carcinoma and to investigate its prognostic value, RNA sequencing, genomic mutation, and clinical data of laryngeal squamous carcinoma patients were collected from the TCGA database. Patients were randomly divided into train and test groups. Cox regression analysis and lasso regression analysis were performed on the data of patients in the training group, and their independent prognostic effect was validated in the test group and the whole cohort. Data from 123 laryngeal squamous carcinoma patients in the TCGA database were collected. According to previous literature, 484 ferroptosis-related genes were collected, and 912 ferroptosis-related lncRNAs were obtained by co-expression. Cox models suggested six lncRNAs involved in ferroptosis (AC083862.2, CYTOR, AC114296.1, LINC02768, GATA2-AS1, CTB-178M22.2). Patients were divided into high-risk and low-risk groups based on median risk scores. Kapkan-Meier survival curve results showed a statistical difference in survival between the high- and low-risk groups. Receiver operating characteristic curves and principal component analysis demonstrated the high accuracy of the model. Univariate and multifactorial Cox regression analyses and column plots demonstrated risk scores as independent prognostic factors. The distribution of prognostic marker risk scores was correlated with clinical staging. Immune infiltration studies suggested the model was associated with immune checkpoints and multiple immune functions. GATA2-AS1 was able to promote cell proliferation, cell migration, and cell invasion. This study identified six lncRNAs associated with ferroptosis in laryngeal squamous carcinoma as prognostic predictors, which may be promising biomarkers involved in the treatment of laryngeal squamous carcinoma.

Construction of an EMT-related lncRNA prognostic signature for lung adenocarcinoma and functional verification of its hub gene LINC01615.

The epithelial-mesenchymal transition (EMT) plays a vital role in the progression of lung adenocarcinoma (LUAD). Long non-coding RNAs (lncRNAs) participate in the EMT process as an important regulatory factor and have the potential to serve as prognostic biomarkers. We aimed to construct a novel lncRNA prognostic signature for LUAD based on EMT-related lncRNAs, identify EMT-related hub lncRNA, and investigate its biological functions. RNA-seq data, clinical and survival information were obtained from The Cancer Genome Atlas database. The EMT-related lncRNA prognostic signature (EMTscore) was constructed using the Least Absolute Shrinkage and Selection Operator Cox regression analysis. The efficiency of EMTscore in predicting the prognosis of LUAD was evaluated through the area under the time-dependent receiver operating characteristic (ROC) curves. The hub lncRNA of the prognostic signature was selected using a co-expression network map, and its effects on cell proliferation and metastasis were explored by in vitro experiments. We constructed a prognostic signature (EMTscore) containing 8 tumor-high expressed lncRNAs. The EMTscore performed well in predicting overall survival rates with AUC values of 0.708 at 5years in the training set. EMTscore could independently predict the survival of LUAD, with HR = 4.011 (95% CI 2.430-6.622) in the multivariate Cox regression. Importantly, we identified LINC01615 as the hub lncRNA in the EMTscore and revealed that LINC01615 enhanced the proliferation, migration, and EMT of lung cancer cells. A new EMT-related lncRNA prognostic signature named EMTscore was developed, and LINC01615 was identified as the hub lncRNA of EMTscore. The hub lncRNA LINC01615 had an oncogenic biological function in LUAD.

A Cuproptosis-Related LncRNA Risk Model for Predicting Prognosis and Immunotherapeutic Efficacy in Patients with Hepatocellular Carcinoma.

Cuproptosis is a novel programmed cell death pathway that is initiated by direct binding of copper to lipoylated tricarboxylic acid (TCA) cycle proteins. Recent studies have demonstrated that cuproptosis-related genes regulate tumorigenesis. However, the potential role and clinical significance of cuproptosis-related long noncoding RNAs (lncRNAs) in hepatocellular carcinoma (HCC) have not been established. We performed a bioinformatics analyses of RNA-sequencing data of HCC patients extracted from The Cancer Genome Atlas (TCGA) dataset to identify and validate a cuproptosis-related lncRNA prognostic signature. Furthermore, we analyzed the clinical significance of the prognostic signature of cuproptosis-related lncRNA in predicting the immunotherapeutic efficacy and the status of the tumor immune microenvironment. The RNA-sequencing data, genomic mutations, and clinical information were downloaded for 374 HCC samples and 50 normal liver samples from TCGA-Liver Hepatocellular Carcinoma (TCGA-LIHC) dataset. Co-expression analysis of Gene-lncRNA pairs with 49 known cuproptosis-related prognostic genes was used to define cuproptosis-related prognostic lncRNAs. We performed the LASSO algorithm and univariate and multivariate Cox regression analysis, respectively, to gradually identify the prognostic risk models of cuproptosis-related lncRNA based on the TCGA-LIHC dataset. Subsequently, the predictive performance of the model was evaluated using receiver operation characteristic (ROC) curves, Kaplan-Meier survival curves, and prognostic nomogram. The analysis of gene-lncRNA co-expression with 49 known cuproptosis-related genes identified 1359 cuproptosis-related lncRNAs in the TCGA-LIHC data set. A prognostic model was constructed with nine cuproptosis-related prognostic lncRNAs (AC007998.3, AC003086.1, AC009974.2, IQCH-AS1, LINC0256 1, AC105345.1, ZFPM2-AS1, AL353708.1 and WAC-AS1) using LASSO regression and Cox regression analyses. Risk scores were calculated for all HCC patient samples based on the four cuproptosis-related lncRNA prognostic models. All HCC patients were divided into high-risk and low-risk subgroups according to a 1:1 ratio column. The Kaplan-Meier survival curve analysis showed that the overall survival rate (OS) of the high-risk group patients was significantly lower than that of the low-risk group. The principal component analysis (PCA) confirmed that the prognostic lncRNA model accurately distinguished between high- and low-risk HCC patients. Furthermore, regression analysis as well as ROC curves confirmed the prognostic value of the risk score. A nomogram with risk scores and other clinicopathological characteristics was constructed. The nomogram accurately predicted the probability of 1-, 3-, and 5-year OS in HCC patients. Tumor mutation burden (TMB) scores were higher for high-risk patients than for low-risk patients. HCC patients in the low-risk group showed lower TIDE scores and greater sensitivity to antitumor drugs than those in the high-risk group. Tumor immune responses and tumor immune cell infiltration were significantly different between the high-risk and low-risk groups of patients with HCC. Our study identified a 9-cuproptosis-related lncRNA signature that accurately predicted prognosis, immunotherapeutic efficacy, and the status of the tumor immune microenvironment in HCC patients. Therefore, this cuproptosis-related lncRNA risk model is a potential prognostic biometric feature in HCC and shows high clinical value in identifying HCC patients who are potentially responsive to immunotherapy.

Identification of pyroptosis-related lncRNA subtype and signature predicts the prognosis in bladder cancer.

Pyroptosis is a new type of programmed cell death involved in all stages of tumorigenesis. Herein, a comprehensive study was conducted to evaluate the prognostic significance of pyroptosis-related lncRNAs in bladder cancer. Consensus clustering analysis was performed to identify the subclusters of bladder cancer. The prognostic pyroptosis-related lncRNA signature was constructed using LASSO Cox regression analysis. Consensus clustering identified 2 clusters of bladder cancer. Interestingly, significant differences in the ESTIMAE score, immune cell infiltration and immune checkpoint expression were obtained between the 2 clusters. A signature consisting of 11 pyroptosis-related lncRNAs was established and it had a good performance in predicting the overall survival rate of bladder cancer, with an AUC of 0.713. Moreover, pyroptosis-related lncRNA signature acted as a risk factor in bladder cancer. Bladder cancer patients with high-risk score had a higher tumor grade and higher clinical stage. A significant correlation was obtained between the risk score and immune cell infiltration. The expression of most checkpoints was higher in bladder cancer patients with high-risk score. A novel pyroptosis-related lncRNA signature was identified with prognostic value for bladder cancer patients. Pyroptosis-related lncRNAs have a potential role in cancer immunology and may serve as prognostic or therapeutic targets in bladder cancer.

A TGF-β signaling-related lncRNA signature for prediction of glioma prognosis, immune microenvironment, and immunotherapy response.

The dysregulation of TGF-β signaling is a crucial pathophysiological process in tumorigenesis and progression. LncRNAs have diverse biological functions and are significant participants in the regulation of tumor signaling pathways. However, the clinical value of lncRNAs related to TGF-β signaling in glioma is currently unclear. Data on glioma's RNA-seq transcriptome, somatic mutation, DNA methylation data, and clinicopathological information were derived from the CGGA and TCGA databases. A prognostic lncRNA signature was constructed by Cox and LASSO regression analyses. TIMER2.0 database was utilized to deduce immune infiltration characteristics. "ELMER v.2" was used to reconstruct TF-methylation-gene regulatory network. Immunotherapy and chemotherapy response predictions were implemented by the TIDE algorithm and GDSC database, respectively. Invitro and invivo experiments were conducted to verify the results and clarify the regulatory mechanism of lncRNA. In glioma, a TGF-β signaling-related 15-lncRNA signature was constructed, including AC010173.1, HOXA-AS2, AC074286.1, AL592424.1, DRAIC, HOXC13-AS, AC007938.1, AC010729.1, AC013472.3, AC093895.1, AC131097.4, AL606970.4, HOXC-AS1, AGAP2-AS1, and AC002456.1. This signature proved to be a reliable prognostic tool, with high risk indicating an unfavorable prognosis and being linked to malignant clinicopathological and genomic mutation traits. Risk levels were associated with different immune infiltration landscapes, where high risk was indicative of high levels of macrophage infiltration. In addition, high risk also suggested better immunotherapy and chemotherapy response. cg05987823 was an important methylation site in glioma progression, and AP-1 transcription factor family participated in the regulation of signature lncRNA expression. AGAP2-AS1 knockdown in invitro and invivo experiments inhibited the proliferation, migration, and invasion of glioma cells, as well as the growth of glioma, by downregulating the expression levels of NF-κB and ERK 1/2 in the TGF-β signaling pathway. A prognostic lncRNA signature of TGF-β signaling was established in glioma, which can be used for prognostic judgment, immune infiltration status inference, and immunotherapy response prediction. AGAP2-AS1 plays an important role in glioma progression.